 Live from the Javits Center in New York City, it's theCUBE, covering Inforum 2017. Brought to you by Infor. Welcome back to Inforum 2017, everybody. This is theCUBE, the leader in live tech coverage. Duncan Angovis, he's the president of Infor and a CUBE alum. Good to see you again, Duncan. Hey, afternoon, guys. So it's all coming together, right? When we first met you guys down in New Orleans, we were sort of unpacking, trying to squint through what the strategy is and now we call it the layer cake we were talking about off-camera, really starting to be cohesive, but set up sort of what's been going on at Infor, how are you feeling, what the vibe is like? Yeah, it's been an amazing journey over the last six years and all the investments we put in products, as we said to you guys way back then, we've always put products at the center. Our belief is that if you put innovation and dramatic amounts of investment in the core product, everything else ends up taking care of itself. And so we put our money where our mouth was, we're a private company so we can be fairly aggressive on the level of investment we put into R&D and it's increased double digit every single year. And I think the results you've seen over the last two years in terms of our financials is that the market's voting in a way that would grow in double digit dramatically faster than our peers. So that feels pretty good. So Jim is, I know dying to get into the AI piece, but let's work our way up that sort of strategy and layer cake with an individual had a lot to do with that. So you know, you guys started with the decision of micro verticals and you know, the interesting thing to us is you're starting to see some of the big SIs join in. And I always joke they love to eat at the trough. And but you took a lot of the food away by doing that last mile. But now you're seeing them come in, why is that? You know, I think the whole industry is evolving, you know, and the roles that different and the value that different companies in that ecosystem play, whether it's an enterprise software vendor or it's a systems integrator, everything's changing. I mean, the cloud was a big part of that. I mean, that took away tasks that you would sometimes see a systems integrator doing as larger companies started to build more completely integrated suites. That took away the notion that you need a systems integrator to plug all those pieces together. And then the last piece for us was all of the modifications that were done to those suites of software to cover off gaps in industry functionality or gaps in localizations for a country should be done inside the software. And you can only do that if you have a deep focus by industry on going super, super deep at a rapid rate on covering out what we call these last mile features. So that means that the role of the systems integrators shifted. I mean, they've obviously pivoted more increasingly into a digital realm. They've all acquired digital agencies. And having to adapt to this world where you have these suites of software that run in the cloud that don't need as much integration or as much customization. So we were there five, six years ago. They weren't quite there. It was still part of this symbiotic relationship with other large vendors. And I think now, the reason for the first time we've got guides like Accenture and Deloitte and Capgemini and Grant Thornton here is that they see that and their business model has evolved. And those guys obviously like to be where they can win business. And I like to build practices around companies that they see winning business. And so the results we've seen and the growth we've seen over the last two to three years obviously that's something they want to piece off. So I think it's going to work out. All right, so Jim, you're going to have to be here with me a second because I want to keep going up the stack. So the second big milestone decision was AWS to run it. And we all understand the benefits of AWS but there's two sides to that coin. And one is, when you show your architectural diagram there's a lot of AWS in there. There's S3, there's DynamoDB. I think I saw Kinesis in there. I'm sure there's some EC2 and other things. And it just allows you to focus on what you do best. At the same time you're getting an increasingly complex data pipeline and ensuring end-to-end performance has to be technically a real challenge for you. So I wanted to ask you about that and see if you could comment and how you're managing that. Yeah, so I mean obviously we were one of the first guys to actually go all in on Amazon as our cloud delivery platform and obviously others now have followed but we're still one of their top five ISVs on there. The only company that Amazon reps actually get compensated on and it's a two-way relationship, right? We're not just using them as a cloud delivery partner. We're also using some of their components. You talked about some of the data storage components we're also leveraging for AI which we'll get into in a second. But it's a two-way relationship. They run our asset management facility for all of their data centers globally. We do all the design and manufacturing of their drones and robots, right? We'll partner with them on the logistics side. So it's a deep two-way relationship but to get to your question on just sort of the volume and the integration. We work in industries with staggering volumes, right? I mean retail, you're dealing with billions and billions of data points and we'll probably get into that in a second, you know. The whole asset management space is one of the fastest growing applications we have driven by the second dynamics of IoT and explosion and device data and all of that. So we've had for a very, very long time had to figure out an efficient way to move large amounts of data that can be highly chatty and do it in an efficient way. And sometimes it's less about the pipes and moving it around. It's how you ingest that data into the right technology from a data storage perspective ingest it and then turn it into insights that can power analytics or feedback into applications to drive execution whether it's us predicting maintenance failure on a pump and then feeding that back into asset management to create a work order and schedule an engineer on it, right? That's not a trivial calculus. Okay, now we're starting to get into Jim's wheelhouse which is you call, I think you call it the age of network intelligence. That's a GT Nexus acquisition. To us, it's all about the data. You got to think you said 18 years of transaction history there. So talk about that layer and then we'll really get into the data, the burst piece and then of course the AI. Yeah, so there were two parts to why we called it the age of network intelligence and it's not often that technology or an idea comes along in human history that actually bends the curve of progress, right? And I think that we said it on stage, the steam engine was one of those and it led to the combustion engine, it led to electricity, it led to the internet and the mobile phone and it all kind of went of course it was invented by a British man, an English man. And that doesn't happen very often, right? Where it does that. And our belief is the rise of networks coupled with the rise of artificial intelligence, those two things together will have the same impact on society and mankind. And it's bigger than in for and bigger than enterprise software. It's going to change everything and it's not going to do it in a linear way. It's going to be exponential. So the network part of that for us from an info perspective was yes, it was about the commerce network, which was GT Nexus and the belief that almost every process you have inside an enterprise at some point has to leave the enterprise. You have to work with someone else, a supplier or a customer. But ERPs in general were designed to automate everything inside the four walls and so our belief was you should extend that and encompass an entire network and that's obviously what the GT Nexus guy spent 18 years building was this idea of this logistics network and this network where you can actually conduct trade and commerce and they do over $500 billion a year on that network. And we believe and we announce this as network cloud suites, those two worlds will blur, right, that ultimately cloud suites will run completely natively on the network and that gives you some very, very interesting information models and the parallel we always give is like a LinkedIn or a Facebook, you know. On LinkedIn, there's one version of the application, right? There's one information model where everyone's contact information is everyone's details of who they are is stored. It's not stored in all these disparate systems that need to be synchronized constantly, right? It's all in one and that's the power of GT Nexus and the commerce network is that we have this one information model for the entire supply chain and now when you move the cloud suite on top of that, it's like this one plus one is five. It's a very, very powerful idea. All right, Jim, chime in here because you and I both excited about the burst when we dug into that a little bit. Quite impressed actually, not lightweight viz. You know, it's not old sort of BI. Well, the next generation of analytics, decision support analytics that infuse and inform and optimize transactions in a distributed value chain and so forth. The burst is a fairly strong team, you got Brad Peters who was on the keynote yesterday and of course did the pre-briefing for the analyst community the day before I think it's really exciting that the Coleman strategy is really an ongoing initiative, of course. First of all, the competitive front, all of your top competitors in this very, I call it a war of attrition in the ERP. SAP Oracle and Microsoft have all made major investments on going in AI across their portfolios with a specific focus on informing and infusing their respective ERP offerings. But what I can see from what in for is announced with the Coleman strategy is that yours is far more comprehensive in terms of taking it across your entire portfolio in a fairly accelerated fashion. I mean, you've already begun to incorporate, it's already, Coleman's already embedded in several of your vertical applications. First question I have for you Duncan, as I was looking through all the discussions around Coleman, when will this process be complete in terms of Colemanizing is my term. Colemanizing the entire cloud suite and of course network cloud suite portfolio. That's a huge portfolio and it's like, you've got fresh funding, a lot of it, from Koch Industries. To what extent can, at what point in the next year or two, can most in for customers have the confidence that their applications, their cloud applications, are Colemanized and then when will, if ever, Coleman AI technology be made available to those customers who are using your premises-based software packages? Yeah. So yeah, we could spend a long time talking about this. So the thing about Coleman and our AI and machine learning capabilities is that we've been at work on it for a while. And we created the Dynamic Science Labs, our team of 65 PhDs based up in MIT, got over three and a half, four years ago. And our differentiation versus all the other guys you mentioned is that we, two things. One, we bring a very application-centric view of it. We're not trying to build a horizontal, generic machine learning platform in the same way that we- Yeah, you're not IBM with WhatsApp and stuff, yeah, yeah. Yeah, yeah, yeah. Or even Oracle or Microsoft or whatever the guy did. Nobody expects you to be. No, you know, and we've always been the guys that have worked with the open source community. Even when you look at, like, we're the first guys to provide a completely open source stack on any of our technology with Postgres. We don't have a dog and the hunt like most of the other guys do, right? So we tap into the innovation that happens in the open source community. And when you look at all the real innovation that's happening in machine learning, it's happening in the open source community. It's not happening with the old legacy, you know, ERP guys. Cancer Flow and Spark and all that stuff, yeah. Google, Apple, Facebook, those are the guys that are doing it. And the academic community is light years ahead on top of that of what these other guys will do. So that's what we tap into, right? Are you tapping into partners like AWS? Because they've obviously got a huge portfolio of AI. Give us a sense whether you're going to be licensing or co-developing Coleman technologies with them going forward. Yeah, so we obviously, we have NDAs with them. We're deeply inside their development organization in terms of working on things. You know, our scientists obviously have presented to them around ideas we think they need to go. I mean, we're a customer of their AI framework to machine learning and we're testing it at scale with specific use cases in industries, right? So we can give them a lot of insights around where it needs to go and problems we're trying to solve. But we do that across a number of different organizations and we've got lots and lots of academic collaborations that happen on around all of the best universities that are pushing on this. We've even received funding from DARPA in certain cases around things that we're trying to solve for. You know, we've made quietly, we've made some machine learning acquisitions over the last five, six years that have obviously brought this capability into it. But the point is, we're going to leverage the innovation that happens around these frameworks and then our job is understanding the industries we're in and that we're an applications company is to bring it to life in these applications in a seamless way that solves a very specific problem in an industry in a powerful and unique way. You know, on stage I talked about this idea of bringing an AI first mindset to how we go about doing it. So it's important if I can interject. This is really important. This is in for IP. The serious R&D that's gone into this, it's innovation, as you know what your competitors are going to say, they're going to deep positions. Ah, it's alexon steroids, but it's not. It's substantial IP and really leveraging a lot of the open source technologies that are out there. Yeah, so, you know, I mean, I talked about there were four components to Coleman, right? And the first part of it was, we can leverage machine learning services to make the cloud suite conversational so they can chat and talk and see and hear and all of that. And yeah, some of those are going to use the technology that sits behind the Lexar and it's available on AWS. Lexar as you guys know, but that's only really a small part of what we're doing. I mean, there are some places where we are looking at using computer vision for example, automated inspection of car rental returns is one area. We're using it for quality management pilot at a company that normally has humans inspect something on a production line. That kind of computer vision, that's not alexa, right? It's, you know, I gave the example of image recognition. Some of it can leverage, you know, AWS's framework there. But again, we're always going to look for the best platform and framework out there to solve the specific problem that we're trying to solve. But we don't do it just for the sake of it. We do it with a focus to begin with, with an industry. Like where's a really big problem we can solve? Or where is there a process that happens inside an application today that if you brought an AI first mindset to it, it's revolutionary. And we use this phrase that AI is the UI. And we've got some pretty good, I've got some pretty good analogies there that I can help bring it to life. And I like your approach for presenting your AI strategy in terms of the value it delivers to your customers, to business. You know, there's this specter out there in the culture that AI is going to automate everybody out of a job. Automation is very much a big part of your strategy, but you've expressed it well. Automating out those repetitive functions so that human beings, you can augment the productivity of human beings, free them up for more value-added activities, and then augment those capabilities through conversational chatbots and so forth and so on. Provide in application, in process, in context decision support with recommendations and all of that. I think that's the exact right way to pitch it. One of the things that we focus on in Wikibon in terms of application development, disciplines that are totally fundamental to this new paradigm, recommendation engines recommender systems in line to all applications. It's happening. I mean, Coleman, that really, in many ways, Coleman will be the silent, not so silent, but it'll be the recommendation engine embedded inside all of your offerings at some point, at least in terms of the strategy you laid out. Yeah, no, absolutely right. I mean, and it's not just about, we all get hung up on machine learning and deep learning because it's the sexy part of AI, right? But there's a lot more. AI all the way back, you can go back to Socrates and the father of logic, right? I mean, some of the things you can do is just based on very complex rules and logic and what used to be called process automation, right? And then it extends all the way to deep learning and neural networks and so on. So one of the things that Coleman also does is it unifies a lot of this technology, things that you would normally do for prediction or optimization. Optimization normally is the province of operations research guys, right? Which again is a completely different field. So it unifies all of that into one consistent platform that has all of that capability into it and then exposes it in a consistent way through our API architecture. So same thing with bots, people always think chat bots is separate. Well, that too is unified inside Coleman. So it's a cohesive platform, but again, industry focused. What's your point of view on developers and how do you approach the development community and what's your strategy there? Yeah, I mean, it's critical, right? So we've always, I mean, hired an incredible number of application engineers every year, the first 12 months we were here, we hired 1800, right? Because, you know, that's kind of what we do. So we believe hugely in smarts and it sounds kind of obvious, but experience can be learned, right? Smarts is portable and we have a lot of programs in place with universities. We call it the Education Alliance Program. And I think we have up to 32 different universities around the world where we're actually influencing curriculum and actually bringing students right out of there using internships during the year and then actually bringing them into our development organization. So we've got a whole pipeline there. I mean, that's, you know, it's critical that we have access to that. And what about outside your four walls or virtual walls of Infor? Is there a strategy to specifically pursue external developers and open up a PaaS layer? Or will you provide an SDK for Coleman, for example, for that, for developers? Yeah, so we did as part of our Infor Operating Service update, which is the name for our Unified Technology Platform. We did announce Mongoose Platform as a service, our Mongoose PaaS. Oh, Mongoose, sure. So that now is being delivered as a platform as a service for application development. And it's used in two ways. It's used for us to build new applications. It's a very mobile first type development framework too. And obviously Hook and Loop had a huge influence in how that ships. The neat thing about it is it ships with plumbing into Ion API, plumbing into our security layer. So customers will use it because it leverages our security model. It's easy to access everything else. But it's also used by our Hook and Loop digital team. So those guys are going off and they're building completely differentiated, curiate apps for customers, right? And again, they're using Mongoose. So I think between our Ion APIs and between all the things you get in the Info Operating Service and Mongoose, we've got a pretty good story around extensibility and application development. As it relates to an SDK for Coleman, you know, we're just working through that now. Again, our number one focus is to build, build those things into the applications. Just, it's a feature. The way most companies have approached optimization and machine learning historically is it's a discrete app that you have to license, it's off to the side and you integrate it in. We don't think that's the right way of doing it. Machine learning and artificial intelligence is a platform. It's an enabler and it fuses and changes every part of the cloud suite, you know? And we've got a great example on how you can rethink demand forecasting, demand planning, right? Every, regardless of the industry we serve, everyone has to predict demand, right? And it's the basis for almost every other decision that happens in the enterprise, right? And how much to make, how many nurses to put on staff, right? All of that, every industry, that prediction of demand. And the thinking there really hasn't changed in 20, 30 years. It really hasn't, you know? And some of that's just because of the constraints with technology, right? Storage, compute, all of that. Well, with the access we have to, you know, elastic supercomputing now and the advance, advancements in sort of machine learning and AI, you can radically rethink all of that and take what we call an AI first approach, which is what we've done with building our brand new demand prediction platform. So the example we gave is you think about when early music players came along on the internet, right? The focus was all around building a gorgeous experience for how to build a playlist, all right? It was drag and drop, I could do it on a phone, I could share it with people, but it was, and it showed pictures of the album art, but it was all around the usability of making that playlist better, right? Then guys like Spotify and Pandora came along and they took an AI first approach to it and the machine builds your playlist. There is no UI, AI is the UI and it can recommend music I never knew I would have liked and the way it does that comes back to the data, which is what I'm going to circle back to in for here in a second, is that it breaks a song down into hundreds if not thousands of attributes about that song, okay? Sometimes it's done by humans, sometimes it's even done by machine listening algorithms. Then you have something that crawls the web, finds music reviews online and further augments it with more and more attributes. Then you layer on top of that, use a listening activity, thumbs up, thumbs down, play, pause, skip, share, purchase, right? And you find at that attribute level, the very lowest level, the true demand drivers of a song and that's what's powering it, right? Just like you see with Netflix for movies and so on. Imagine bringing that same thought process into how you predict demand for items that you've never promoted before, right? Never changed the price before, never put in this store before, never seen before. The cold start problem and building recommendation agents. Exactly right, so that's what we mean by AI first. It's not about just taking traditional demand, planning approaches and making it look sexier and putting it on an iPad, right? Rethink it. Well, it's been awesome to watch, we are out of time, it's been awesome to watch the evolution of Inforas. It's really becoming a data company and we love having executives like you on. You know, super articulate, you got technical chops, congratulations on the last six years, the little quasi-exit you guys had and look forward to watching the next six to 10. So thanks very much for coming on. Really, thank you guys. All right, thank you. All right, keep it right there, we'll be back with our next guest. This is Inform 2017 and this is theCUBE. We'll be right back.