>> Announcer: Live from the Javits Center in New York City, it's theCUBE. Covering Inforum 2017. Brought to you buy Infor. >> Welcome back to Inforum 2017 everybody. This is theCUBE, the leader in live tech coverage. Duncan Angove is here, 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. 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, um, you know, all the investments we put in products, as you know, 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 we put our money where our mouth was. You know, 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, you know the market's voting in a way that we're growing double digits dramatically faster than our peers. So that feels pretty good. >> So Jim is, I know, dying to get into the AI piece, but lets work our way up that sort of strategy 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 SI's join in. And I always joke, that they love to eat at the trough. But you took a lot of the food away by doing that last mile. >> Yeah. >> But now you're seeing them come in, why is that? >> You know I think the whole industry is evolving. And the roles that different and the valor 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. 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 malfeatures. So that means that the role of the systems integrators shifted. I mean they've obviously pivoted more recently 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 you know 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, you know, the reason for the first time we've got guys like Accenture, and Deloitte, and Capgemini, and Grant Thornton here, is that they see that. And their business model's evolved. And you know those guys obviously like to be where they can win business and like to build practices around companies they see winning business. 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 a piece of. So I think it's going to work out. >> Alright so Jim, you're going to have to bear with me a second 'cause I want to keep going up the stack. So the second big milestone decision was AWS. >> Duncan: Yeah. >> And we all understand the benefits of AWS. But there's two sides to that cone 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 a Cloud delivery platform. And obviously others now have followed. But we're still one of their top five ISV's 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 know you talked about some of their data storage components. We're also leverage them for AI which we'll get into in a second. But it's a two way relationship. You know, 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. We're partnered with them on the logistic 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 integrations 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 cycle dynamics of IoT and explosion in 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 in 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 feed back into our 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 it, I think you call it the "Age of Network Intelligence". And that's the GT Nexus acquisition. >> Yeah. >> To us it's all about the data. I 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 lead to the combustion engine, it lead to electricity and it lead to the internet and the mobile phone and it all kind of went. Of course it was invented by a British man, an Englishman you know? That doesn't happen very often right? Where it does that. And our belief is that 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 Infor 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 Infor 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 ERP's in general, were designed to automate everything inside the four walls. So our belief was that you should extend that and encompass an entire network. And that's obviously what the GT Nexus guys spent 18 years building was this idea of this logistics network and this network where you can actually conduct trade and commerce. They do over 500 billion dollars a year on that network. And we believe, and we've announced this as network CloudSuites, that those two worlds will blur. Right? That ultimately, CloudSuites will run completely nakedly 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. On Linkedin, there's one version of the application. Right? There's one information model where everyone's contact information is. Everyone's details about 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 CloudSuite on top of that, it's like this one plus one is five. It's a very, very powerful idea. >> Alright Jim, chime in here, because you and I both excited about the burst when we dug into that a little bit. >> Yes. >> Quite impressed actually. Not lightweight vis, you know? It's not all 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 for the burst is a fairly strong team, you've 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, the Coleman strategy is really an ongoing initiative of course. First of all, on the competitive front, all of your top competitors in this very, I call it a war of attrition in 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 conceived from what Infor's 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, 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 CloudSuite and of course network CloudSuite portfolio. That's a huge portfolio. And it's like you 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 Infor customers have the confidence that 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? >> So yeah, we could spend a long time talking about this. The thing about Coleman and RAI and machine learning capabilities is that we've been at work on it for a while. And you know we created the dynamic science labs. Our team of 65 Ph.D.'s based up in M.I.T. got over three and a half four years ago. And our differentiation versus all the other guys you mentioned is that, 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 Watson, all that stuff. >> Yeah, no, no. Or even Auricle. >> Jim: Understood. >> Or Microsoft. >> Jim: Nobody expects you to be. >> No, you know, and we've always been the guys that have worked for the Open Source community. Even when you look at like, we're the first guys to provide a completely open source stack underneath our technology with postscripts. We don't have a dog in the hunt like most of the other guys do. Right? So we tap in to 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. >> Jim: Yes. >> It's not happening with the old legacy, you know, ERP guys. >> Jim: Pencer, Flow and Spark and all that stuff. >> Yeah, Google, Apple, the GAFA. >> Yeah. >> Right? 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? 'Cause they've obviously, >> Duncan: Absolutely >> got a huge portfolio of AI. >> Yeah, so we. >> 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 NDA's with them, we're deeply inside their development organization in terms of working on things. You know, our science is obviously presented to them around ideas we think they need to go. I mean, we're a customer of their AI frameup 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 DAPA in certain cases around things that we're trying to solve for. You know 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 this AI first mindset to how we go about doing it. >> So it's important, if I can interject. This is very important. This is Infor IP, the serious R&D that's gone into this. It's innovation. 'Cause you know what your competitors are going to say. They're going to deposition and say, oh, it's Alexa on 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 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 CloudSuites 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 Alexa. And it's available in AWS's Alexa as you guys know. But that's only really a small part of what we're doing. 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 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, "the AI is the UI". And we've got some pretty good analogies there that can help bring it to life. >> And I like your approach for presenting your AI strategy, in terms of the value it delivers your customers, to business. You know, there's this specter out there in the culture that AI's going to automate everybody out of a job. Automation's very much a big part of your strategy but you 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 chat box. And so forth, and so on. Provide you know, in-application, in process, in context, decision support with recommendations and all that. I think that's the exact right way to pitch it. One of the things that we focus on and work on in terms of application development, disciplines that are totally fundamental to this new paradigm. Recommendation engines, recommender systems, in line to all application. It's happening, I mean, Coleman, that really in many ways, Coleman will be the silent, well 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. It's not just about, we all get hung up on machine-learning and deep learning 'cause it's the sexy part of AI, right? But there's a lot more. I mean, AI, all the way back, you can go all the way 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, and optimization normally is the province of operations research guys right? Which again it's a completely different field. So it unifies all of that into one consistent platform that has all of that capability into it. And then it exposes it in a consistent way through our API architecture. So same thing with bots. People always think chat bots are 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. I think the first 12 months we were here, we hired 1800 right? 'Cause 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, 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 critical that we have access to those. >> And what about outside your four walls, or virtual walls have been four? Is there a strategy to specifically pursue external developers and open up a PAZ layer? >> Yeah we do. >> Or provide an STK for Coleman for example, for developers. >> Yeah so we did, as part of our Infor Operating Service update. Which is, you know, the name for our unified technology platform. We did announce Mongoose platform was a service. Our Mongoose pass. >> Host: Oh Mongoose, sure. >> So that now is being delivered as a platform with 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 that 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 curated apps for customers. And again, they're using Mongoose. So I think between ION API's and between all the things you get in the Infor Operating Service, and Mongoose, we've got a pretty good story around extensibility and application development. As it relates to an STK for Coleman, we're just working through that now. Again, our number one focus is to build those things into the applications. 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. And 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 CloudSuite. And we've got a great example on how you can rethink demand forecasting, demand planning. Every, regardless of the industry we serve, everyone has to predict demand right? It's the basis for almost every other decision that happens in the enterprise. And, how much to make, how many nurses to put on staff, 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. And some of that's just because of the constraints with technology. Storage, compute, all of that. Well with the access we have to the elastic super-computing now and the advancements in sort of machine-learning and AI, you can radically rethink all of that, and take what we call and "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. It was drag and drop, I could do it on a phone, I could share it with people and it showed pictures of the album art. But it was all around the usability of making that playlist better. Then guys like Spotify and Pandora came around and it 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've liked. And the way it does that, comes back to the data. Which is why I'm going to circle back to Infor here in a second. Is that, it breaks a song down into hundreds if not thousands of attributes about that song. Sometimes it's done by a human, 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, user listening activity, thumbs up, thumbs down, play, pause, skip, share, purchase. 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. Never changed the price before. Never put in this store before. Never seen before. >> The cold start problem in billing recommendation areas. >> 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. >> Yeah, we're out of time. >> Been awesome to watch the evolution, >> We could go on and on with this yeah. >> of Infor as it's really becoming a data company. And we love having executives like you on. >> Yeah >> You know, super articulate. You got technical chops. Congratulations on the last six years. >> Thanks. >> The sort of quasi-exit you guys had. >> Great show, amazing turnout. >> And look forward to watching the next six to 10. So thanks very much for coming out. >> Brilliant, thank you guys. Alright thank you. >> Alright keep it right there everybody, we'll be back with our next guest, this is Inforum 2017 and this is theCUBE. We'll be right back. (digital music)

(upbeat music) >> Announcer: Live from the Javits Center in New York City. It's the Cube. Covering Inforum 2017. Brought to by Infor. >> Welcome back to the cube's coverage of Inforum here at the Javits center in New York City. I'm your host Rebecca Knight along with my co-host Dave Vellante, and Jim Kobielus who is the lead analyst for Wikibon in AI. So guys we're wrapping up day one of this conference. What do we think? What did we learn? Jim you've been, we've been here at the desk, interviewing people, and we've certainly learned a lot from them, but you've been out there talking to people, and off the record I should say. >> Yeah. >> So give us. >> I'm going to name names. >> Yes. >> If I may, I want to clarify something. >> Yeah, okay, sorry. >> I said this morning that the implied valuation was like three point seven, three point eight billion. >> Rebecca: Okay. >> Charles Phillips indicated to us off camera actually it was more like 10 and a half billion. >> Yeah, yeah. >> But I still can't make the math work. So I'm working on that. >> Okay. >> I suspect what's happened, was that a pre debt number. Remember they have a lot of debt. >> Yes. >> So I will figure it out, find out, and report back, okay. >> You do. >> So I just wanted to clarify that. >> Run those numbers okay. >> I'll call George. >> Kay, right, but Jim back to you. What do think is the biggest impression you have of the day in terms of where Infor is? >> Yeah, I've had the better part of this day to absorb the Coleman announcement which of course, ya know AI is one my core focus areas at Wikibon, and it really seems to me that, well Infor's direct competitors are the ERP space of all in cloud it's SAP, it's Oracle, it's Microsoft. They all have AI investments strategies going for in their ERP portfolios. So I was going back, and doing my own research today, just to get my head around where does Coleman put Infor in the race, cause it's a very competitive race. I referred to it this morning maybe a little bit extremely as a war of attrition, but what I think is that Coleman represents a milestone in the development of the ERP cloud, ERP market. Where with SAP, Oracle, and Microsoft, they're all going deep on AI and ERP, but none of them has the comprehensive framework or strategy to AI enable their suites for human augmentation, ya know, natural language processing, conversational UI's, Ya know, recommenders in line to the whole experience of ya know inventory management, and so forth. What infor has done with Coleman is laid out a, more than just a framework and a strategy, but they've got a lot of other assets behind the whole AI first strategy, that I think will put in them in good steady terms of innovating within their portfolio going forward. One of which is they've got this substantial infusion of capital from coke industries of course, and coke is very much as we've heard today at this show very much behind where the infor team under Charles is going with AI enabling everything, but also the Burst team is now on board with it, and the acquisition closed last month Brad Peters spoke this morning, and of course he spoke yesterday at the analyst pre-brief, and so David and I have more than 24 hours to absorb, what they're saying about where Burst fits into this. Burst has AI assets all ready. That, ya know Infor is very much committed to converging the best of what Burst has with where Coleman is going throughout their portfolio. What Infor announced this morning is all of that. Plus the fact that they've already got some Colemanize it's a term I'm using, applications in their current portfolio. So it's not just a future statement of direction. It's all that they've already done. Significant development and productization of Coleman, and they've also announced a commitment Infor with in the coming year, to bring, to introduce Coleman features throughout each of the industry vertical suite, cloud suites, like I said, human augmentation, plus automation, plus assistants, that are ya know, chat bots sort of inline. In other words, Infor has a far more ambitious and I think, potentially revolutionary strategy to really make ERP, to take ERP away from the legacy of protecters that have all been based on deterministic business rules, that a thicket, a rickety thicket of business rules that need to be maintained. Bringing it closer to the future of cognitive applications, where the logic will be in predictive, and deterministic, predictive, data driven algorithms that are continually learning, continually adapting, continually optimizing all interactions and transactions that's the statement of direction that I think that Infor is on the path to making it happen in the next couple of years in a way that will probably force SAP, Oracle, Microsoft to step up their game, and bring their cognitive or AI strategies in portfolios. >> So I want to talk some more about the horse in the track, but I want to still understand what it is. >> Jim: Yes. >> So the competitors are going to say is oh. It's Alexa. Okay, okay it is partially. >> Jim: Yeah sure. It's very reductive that's their job to reduce. >> Yeah you're right, you've lived that world for a while. Actually that was not your job, so. >> If you don't understand technology, you're just some very smart guy who talks a good talk. >> Yeah, okay. >> So, yeah. >> So, okay, so what we heard yesterday in the analyst meeting, and maybe you found this out today, was is conversational UX. >> Yes. >> It's chat wired into the APIs, and that's table stakes. It augments, it automates, an example is early payments versus by cash on hand. Should I take the early payment deal, and take the discount, or, and so it helps decide those decisions, and which can, if you have a lot of volume could be complex, and it advises it uncovers insights. Now what I don't know is how much of the IP is ya know, We'em defense essentially from Amazon, and how much is actual Infor IP, ya know. >> Good question, good question, whether it's all organically developed so far, or whether they've sourced it from partners, is an open issue. >> Question for Duncan Demarro. >> Duncan Demarra, exactly. >> Okay, so who are the horses in the track. I mean obviously there's Google, there's Amazon, there's I guess Facebook, even though they're not competing in the enterprise, there's IMB Watson, and then you mentioned Oracle, and SAP. >> Well, here's the thing. You named at least one of those solution providers, IBM for example, provides obviously a really sophisticated, cognitive AI suite under Watson that is not imbedded however, within an ERP application suite from that vendor. >> No it's purpose built for whatever. >> It's purpose built for stand alone deployment into all manner of applications. What Infor is not doing with Coleman, and they make that very clear, they're not building a stand alone AI platform. >> Which strategy do you like better. >> Do I like? They're both valid strategies. First of all, Infor is very much a sass vendor, going forward in that they don't they haven't given any indications of going into past. I mean that's why they've partnered with Amazon, for example. So it's clear for a sass vendor like Infor going forward to do what they've done which is that they're not going to allow their customers apparently to decouple the Coleman infrastructure from everything else that ya know, Infor makes money on. >> Which for them is the right strategy. >> Yeah, that's the right strategy for them, and I'm not saying it's a bad strategy for anybody who wants to be in Infor's market. >> So what is in Oracle, or in a SAP, or for that matter, a work day do, I mean service now made some AI announcements at their knowledge event. So they're spending money on that. I think that was organic IP, or I don't know maybe they're open swamps AI compenents. >> Sure, sure, A they need to have a cloud data platform that provides the data upon which to build and train the algorithm. Clearly Infor has cast a slot with AWS, ya know, SAP, Microsoft, Orcale, IBM they all have their own cloud platform. So >> And GT Nexus plays into that data corpus or? >> Yeah, cause GT Nexus is very much a commerce network, ya know, and there is EDI for this century, that is a continual free flowing, ever replenishing, pool of data. Upon which to build and train. >> Okay, but I interrupted you. You said number one, you need the cloud platform with data. >> Ya need the conversational UI, you know, the user reductive term chat bots, ya know, digital assistant. You need that technology, and it ya know, it's very much a technology in the works, its' not like. Everybody's building chat bots, doesn't mean that every customer is using them, or that they perform well, but chat bots are at the very heart of a new generation of application development conversational interfaces. Which is why Wikibon, why are are doing a study, on the art of building, and training, and tuning chat bots. Cause they are so fundamental to the UX of every product category in the cloud. >> Rebecca: And only getting more so. >> IOT, right, desk top applications. Everything's going with , moving towards more of a conversational interface, ya know. For starters, so you need a big data cloud platform. You need a chat bot framework, for building and ya know, the engagement, and ya know, the UI and all of that. You need obviously, machine learning, and deep learning capabilities. Ya know, open source. We are looking at a completely open source stack in the middle there for all the data. Ya know, you need obviously things like tenserflow for deep learning. Which is becoming the standard there. Things like Spark, ya know, for machine learning, streaming analytics and so forth. You need all that plumbing to make it happen, but you need in terms of ERP of course, you need business applications, and you need to have a business application stacked to infuse with this capability, and there's only a hardcore of really dominant vendors in that space. >> But the precious commodity seems to be data. >> Yeah. >> Right. >> Precious commodity is data both to build the algorithms, and an ongoing basis to train them. Ya see, the thing is training is just as important as building the algorithms cause training makes all the difference in the world between whether a predictive analytics, ya know ML algorithm actually predicts what it's supposed to predict or doesn't. So without continual retraining of the algorithms, they'll lose their ability to do predictions, and classifications and pattern recognitions. So, ya know, the vendors in the cloud arena who are in a good place are the Googles and the Facebooks, and others who generate this data organically as part of their services. Google's got YouTube, and YouTube is mother load of video and audio and so forth for training all the video analytics, all the speech recognition, everything else that you might want to do, but also very much, ya know, you look at natural language processing, ya know, text data, social media data. I mean everybody is tapping into the social media fire hose to tune all the NLP, ongoing. That's very, very important. So the vendor that can assemble a complete solution portfolio that provides all the data, and also very much this something people often overlook, training the data involves increasingly labeling the data, and labeling needs a hardcore of resources increasingly crowdsource to do that training. That's why companies like Crowd Flower, and Mighty AI, and of course Amazon with mechanical terf are becoming evermore important. They are the go to solution providers in the cloud for training these algorithms to keep them fit for purpose. >> Mmm, alright Rebecca, what are your thoughts as a sort of newbie to Infor. >> I'm a newbie yes, and well to be honest, yes I'm a newbie, and I have only an inch wide, an inch deep understanding of the technology, but one thing that has really resonated with me. >> You fake it really well. >> Well, thank you, I appreciate that, thank you. That I've really taken away from this is the difficulties of implementing this stuff, and this what you hear time and time again. Is that the technology is tough, but it's the change management piece that is what trips up these companies because of personalities who are resistant to it, and just the entrenched ways of doing things. It is so hard. >> Yes, change management, yes I agree, there's so many moving parts in these stacks, it's incredible. >> Rebecca: Yeah. >> If you we just focus on the moving parts that represent the business logic that's driving all of this AI, that's a governance mess in it's own right. Because what you're governing, I mean version controls and so forth, are both traditional business rules that drive all of these applications, application code, plus all of these predictive algorithms, model governance, and so forth, and so on. I mean just making sure that all of that is, you're controlling versions of that. You've got stewards, who are managing the quality of all that. Then it moves in lock step with each other so. >> Rebecca: Exactly. >> So when you change the underlying coding of a chat bot, for example, you're also making sure to continue to refresh and train, and verify that the algorithms that were built along with that code are doing their job, so forth. I'm just giving sort of this meta data, and all of that other stuff that needs to be managed in a unified way within, what I call, a business logic governance framework for cloud data driven applications like AI. >> And in companies that are so big, and where people are so disparately located, these are the biggest challenges that companies are facing. >> Yeah, you're going to get your data scientists in lets say China to build the deep learning algorithms, probably to train them, your probably going to get coders in Poland, or in Uruguay or somewhere else to build the code, and over time, there'll be different pockets of development all around the world, collaborating within a unified like dev ops environment for data science. Another focus for us by the way, dev ops for data science, over time these applications like any application, it'll be year after year, after year of change and change. The people who are building and tuning and tweaking This stuff now probably weren't the people five years ago, as this stuff gets older, who built the original. So you're going to need to manage the end to end life cycle, ya know like documentation, and change control, and all that. It's a dev ops challenge ongoing within a broader development initiative to keep this stuff from flying apart from the sheer complexity. >> Rebecca: Yes. >> So, just I don't Jim, if you can help me answer this, this might be more of a foyer sort of issue, but when we heard from the analyst meeting yesterday, Soma, their chief technical guy, who's been on the Cube before in New Orleans, very sharp dude, Two things that stood out. Remember that architecture slide, they showed? They showed a slide of the XI and the architecture, and obviously they're building on AWS cloud. So their greatest strengths are in my view, any way the achilles heel is here, and one is edge. Let's talk about edge. So edge to cloud. >> Jim : Yes. >> Very expensive to move data into the cloud, and that's where ya know, we heard today that all the analysis is going to be done, we know that, but you're really only going to be moving the needles, presumably, into the cloud. The haystacks going to stay at the edge, and the processing going to be done at the edge, it's going to be interesting to see how Amazon plays there. We've seen Amazon make some moves to the edge with snowball, and greenfield and things like that, and but it just seems that analytics are going to happen at the edge, otherwise it's going to be too expensive. The economic model doesn't favor edge to cloud. One sort of caveat. The second was the complexity of the data pipeline. So we saw a lot of AWS in that slide yesterday. I mean I wrote down dynamo DB, kineses, S3 redshift, I'm sure there's some EC2. These are all discreet sort of one trick pony platforms with a proprietary API, and that data pipeline is going to get very, very complex. >> Flywheel platforms I think when you were talking to Charles Phillips. >> But when you talk to Andy Jasse, he says look we want to have access to primitive access to those APIs. Cause we don't know what the markets going to do. So we have to have control. It's all about control, but that said, it's this burgeoning collection of at least 10 to 15 data services. So the end to end, the question I have is Oracle threw down the gauntlet in cloud. They said they'll be able to service any user request in a 150 milliseconds. What is the end to end performance going to be as that data pipeline gets more robust, and more complicated. I don't know the answer to that, but I think it's something to watch. Can you deliver that in under 150 milliseconds, can Oracle even do that, who knows? >> Well, you can if you deliver more of the actual logic, ya know, machine learning and code to the edge, I mean close the user, close to the point of decision, yes. Keep in mind that the term pipeline is ambiguous here. One one hand, it refers, in many people's minds to the late ya know, the end to end path of a packet for example, from source to target application, but in the context of development or dev ops it refers to the end to end life cycle of a given asset, ya know, code or machine learning, modeling and so forth. In context of data science in the pipeline for data science much of the training the whole notion of training, and machine learning models, say for predictive analysis that doesn't happen in real time in line to actual executing, that happens, Ya know, it happens, but it doesn't need it's not inline in a critical path of the performance of the application much of that will stay in the cloud cause that's massively parallel processing, of ya know, of tensorflow, graphs and so forth. Doesn't need to happen in real time. What needs to happen in real time is that the algorithms like tensorflow that are trained will be pushed to the edge, and they'll execute in increasingly nanoscopic platforms like your smartphone and like smart sensors imbedded in your smart car and so forth. So the most of the application logic, probabilistic ya know, machine learning, will execute at the edge. More of the pipeline functions like model building, model training and so forth, data ingest, and data discovery. That will not happen in real time, but it'll happen in the cloud. It need not happen in the edge. >> Kind of geeky topics, but still one that I wanted to just sort of bring up, and riff on a little bit, but let's bring it back up, and back into sort of. >> And this is the thing there's going to be a lot more to talk about. >> Geeking out Rebecca, we apologize. >> You do indeed, it's okay, it's okay. >> Dave indulges me. >> No, you love it too. >> Of course, no I learn every time I try to describe these things, and get smart people like Jim to help unpack it, and so. >> And we'll do more unpacking tomorrow at two day of Inforum 2017. Well, we will all return. Jim Kobielus, Dave Vellante, I'm Rebecca Knight. We will see you back here tomorrow for day two. (upbeat music)

>> Announcer: Live from the Javits Center in New York City, it's theCUBE! Covering Inforum 2017. Brought to you by Inforum. >> Welcome to day one of theCUBE's coverage of Inforum here at the Javits Center in New York City. I'm your host, Rebecca Knight, along with my co-host, Dave Vellante. We are also joined by Jim Kobielus, who is the lead analyst for artificial intelligence at Wikibon. Thanks so much. It's exciting to be here, day one. >> Yeah, good to see you again, Rebecca. Really, our first time, we really worked a little bit at Red Hat Summit. >> Exactly, first time on the desk together. >> It's our very first time. I first met you a little while ago, and already you're an old friend. >> This is the third time we've done Inforum. The first time we did it was in New Orleans, and then Infor decided to skip a year. And then, last year, they decided to have it in the middle of July, which is kind of a strange time to have a show, but there are a lot of people here. I don't know what the number is, but it looks like several thousand, maybe as many as 4000 to 5000. I don't know what you saw. >> Rebecca: No, no, I feel like this is a big show. >> Jim: Heck, for July? For any month, actually. >> Exactly, particularly at a time where we're having a lot of rail issues, issues at LaGuardia too, so it's exciting. >> theCUBE first met Infor at the second Amazon re:Invent. I remember the folks at Amazon told us, "We really have an exciting SAS company. "It's the largest privately-held SAS company in the world." We were thinking, is that SAS? And they said, "No, no, it's a company called Infor." We said, "Who the heck is Infor?" And then we had Pam Murphy on. That's when we first were introduced to the company, and then, of course, we were invited to come to New Orleans. At the time, the questions around Infor were, who is Infor? What are they all about? And then it became, okay, we started to understand the strategy a little bit. For those of you who don't familiar with Infor, their strategy from early on was to really focus on the micro-verticals. We've talked about that a little bit. Just a quick bit of history. Charles Phillips, former president of Oracle, orchestrator of the M&A at Oracle, PeopleSoft, Siebel and many others, left, started Infor to roll up, gold-funded by Golden Gate Capital and other private equity, substantial base of Lawson Software customers, and then, many, many other acquisitions. Today, fast forward, you got a basically almost $3 billion company with a ton of debt, about $5 billion in debt, notwithstanding the Koch brothers' investment, which is almost $2.5 billion, which was to retire some of the equity that Golden Gate had, some of the owners, Charles and the three other owners took some money off the table, but the substantial amount of the investment goes into running the company. Here's what's interesting. Koch got a 2/3 stake in the company, but a 49% voting share, which implies a valuation of about, I want to say, just under four billion. Let's call it 3.7, 3.8 billion. For a $2 billion to $3 billion company, that's not a software company with 28% operating margin. That's not a huge valuation. So, we'll ask Charles Phillips about that, I mean, some of this wonky stuff in the financials, you know, we want to get through. I'm sure Infor doesn't want to talk too much about that. >> But it is true. It is, for a unicorn, for a privately-held company, this is one of them. This is up there with Uber and Airbnb, and it's a question that, why isn't it valued at more? >> My only assumption here is they went to Koch and said, "Okay, here's the deal. "We want $2 billion plus. "You only get 49%, only. "If you get 49% of the company in terms of voting rights, "we'll give you 2/3 in terms of ownership. "It's a sweetheart deal. "Of course, it's a lot of dough. "You get a board seat." Maybe two board seats, I can't remember. "And we'll pump this thing up, we'll build up the equity, "and we'll float it someday in the public markets, "and we'll all make a bunch of dough "and our shareholders will all be happy." That's the only thing I can assume, was this sort of conversation that went on. Well, again, we'll ask Charles Phillips, see if he answers that. But James, you sat in yesterday at the analyst event, you got sort of the history of the company, and the fire hose of information leading up to what was announced today, Coleman AI. What were your impressions as an analyst? >> Well, first of all, my first impression was a thought, a question. Is Infor with Coleman AI simply playing catch-up in a very, I call it a war of attrition in the ERP space. Really, it's four companies now. It's SAP, it's Microsoft, it's Oracle, and it's Infor duking it out. SAP, Microsoft and Oracle all have fairly strong AI capabilities and strategies and investments, and clearly they're infused, I was at Microsoft Build a few months ago. They're infusing those capabilities into all of their offerings. With Coleman, sounds impressive, thought it's just an early announcement, they've only begun to trickle it out to their vast suite. I want to get a sense, and probably later today we'll talk to Mr. Angove, Duncan Angove. I want to get a sense for how does, or does, Infor intend to differentiate their suite in this fiercely competitive ERP world? How will Coleman enable them to differentiate it? Right now it seems like everything they're announcing about Coleman is great in terms of digital assistance, conversational interface, everybody does this, too, now, with chatbots and so forth, in-line providing recommendations. Everybody's doing that. Essentially, everybody wants to go there. How are they going to stand apart with those capabilities, number one? Number two is just the timeline. They have this vast suite, and we just came from the keynote, where Charles and the other execs laid out in minute detail the micro-vertical applications. What is their timeline for rolling out those Coleman capabilities throughout the suite so customers can realize they have value? And is there a layered implementation? They talked about augmentation versus automation, and versus assistance. I'd like to see sort of a layer of capabilities in an architecture with a sense for how they're going to invest in each of those capabilities. For example, they talked about open source, like with TensorFlow, which is a new deep learning framework from Google Open Source. I just want to get a deep dive into where the investment funds that they're getting from Koch and others, especially from Koch, where that's going in terms of driving innovation going forward in their portfolio. I'm not cynical about it, I think they're doing some really interesting things. But I want some more meat on the bones of their strategy. >> Well, it's interesting, because I think Infor came into the show wanting to message innovation. They're not known as an innovative company. But you heard Charles Phillips up there talking, today he was talking about quantum computing, he was talking about the end of Moore's Law, he was obviously talking about AI. They named Coleman after Katherine Coleman Johnson. >> Here's my speculation. My speculation, of course, they recently completed the acquisition of Birst. Brad Peters did a really good discussion of Birst, the BI startup that's come along real fast. My sense, and I want to get confirmation, is that, possibly, Birst and Brad Peters and his team, will they drive the Coleman strategy going forward? It seems likely, 'cause Birst has some AI assets that Brad Peters brought us up to speed on yesterday. I want to get a sense for how Birst's AI and Coleman AI are going to come together into a convergence. >> But wouldn't they say that it's quote-unquote embedded, embedded AI? >> Jim: It'll be invisible, it has to be. >> You know, buried within the software suite? We saw, like you said, in gory detail the application portfolio that Infor had. I think one of the challenges the company has, it's like some of my staff meetings. Not everything is relevant to everybody. Very clearly, they have a lot of capabilities that most people aren't aware of. The question is, how much can they embed AI across those, and where are the use cases, and what's the value? And it's early days, right? >> Oh, yeah, very much. And you know, in some of those applications, probably many of them, the automation capabilities that they described for Coleman will be just as important as the human augmentation capabilities. In other words, micro-verticalize their AI in diverse ways going forward across their portfolio. In other words, one AI brush, broad brush of AI across every application probably won't make sense. The applications are quite different. >> I want to talk about the use cases, here. The selling points for these things are making the right decision all the time, more quickly. >> Jim: Productivity accelerators for knowledge workers, all that. >> And one of the other points that was made is that there are fewer arguments, because we are all looking at the same data, and we trust the data. Where do you see Birst and Coleman? Give me an example of where you can see this potentially transforming the industry? >> "We all trust data." Actually, we don't all trust data, because not all data is created the same. Birst comes into the portfolio not just to, really great visualizations and dashboarding and so forth, but they've got a well-built data management backend for data governance and so forth, to cleanse the data. 'Cause if you have dirty data, you can't derive high-quality decisions from the data. >> Rebecca: Excellent point, right. >> That's really my general take on where it's going. In terms of the Birst, I think the Birst acquisition will become pivotal in terms of them taking their data-driven functionality to the next level of consumability, 'cause Birst has done a really good job of making their capability consumable for the general knowledge worker audience. >> Well, a couple things. Actually, let me frame. Charles Phillips, I thought, did a good job framing the strategy. Sort of his strategy stack, if you will, starting with, at the bottom of the stack, the micro-verticals strategy, and then moving up the next layer was their decision to go all cloud, AWS Cloud. The third was the network. Infor made an acquisition of a company called GT Nexus, which is a commerce platform that has 18 years of commerce data and transaction data there. And the next layer was analytics, which is Birst, and I'll come back to that. And then the top layer is Coleman AI. The Birst piece is interesting, because we saw the ascendancy of Tableau and its land-and-expand strategy, and Christian Chabot, the CEO of Tableau, used to talk about, and they said this yesterday, the slow BI, you know, cubes, and the life cycle of actually getting an answer. By the time you get the answer, the market has changed. And that's what Tableau went after, and Tableau did very, very, well. But it turned out Tableau was largely a desktop tool. Wasn't available in the Cloud. It is now. And it had its limitations. It was basically a visualization tool. What Infor has done with Birst is they're positioning the old Cognos, which is now IBM, and the micro strategies of the world as the old guard. They're depositioning Tableau, and they didn't use that specific name, Tableau, but that's what they're talking about, Tableau and Click, as less than functional. Sort of spreadsheet plus. And they are now the rich, robust platform that both scales and has visualization, and has all the connections into the enterprise software world. So I thought it was interesting positioning. Would love to talk to some customers and see what that really looks like. But that, essentially, was the strategy stack that Charles Phillips laid out. I guess the last point I'd make as I come back to the decision to go AWS, you saw the application portfolio. Those are hardcore enterprise apps which everybody says don't live in the Cloud. Well, 55% of Infor's revenue is from the Cloud, so, clearly, it's not true. A lot of these apps are becoming cloud-enabled. >> Jim: Yeah, most of them. >> Most of them? >> Most of them are, yeah. BI, mode-predictive analytics, most AI. Machine learning is going in the Cloud. >> 'Cause Oracle's argument is, Oracle will be only one who can put those apps in the Cloud. >> 'Cause the data lives in the Cloud. It's trained on the data. >> Not all the data lives in the Cloud. >> It's like GT Nexus. That's EDI, that's rich EDI data, as they've indicated for training this new generation of neutral networks, machine learning and deep learning models continuously from fresh transaction data. You know that's where GT Nexus and e-commerce network fits into this overall strategy. It's a massive pile stream of data for mining. >> But, you know, SAP has struggled in the Cloud. SuccessFactors, obviously, is their SAS play. Most of their stuff remains on-prem. Oracle again claims they have the only end-to-end hybrid. You see Microsoft finally shipping Azure Stack, or at least claiming to soon be shipping Azure Stack. They've obviously got a strategy there with their productivity estate. But here you have Infor-- >> Don't forget IBM. They've got a very rich, high-rated portfolio. >> Well, you heard, I don't know if it was Charles, somebody took a swipe at IBM today, saying that the company's competitors have purchased all these companies, these SAS companies, and they don't have a way to really stitch them together. Well, that's not totally true. Bluemix is IBM's way. Although, that's been a heavy lift. We saw with Oracle Fusion, it took over a decade and they're still working on that. So, Infor, again, I want to talk to customers and find out, okay, how much of this claim that everything's seamless in the Cloud is actually true? I think, obviously, a large portion of the install base is still that legacy on-prem Lawson base that hasn't modernized. That's always, in my view, enforced big challenges. How do you get that base, leverage that install base to move, and then attract new customers? By all accounts, they're doing a pretty good job of it. >> I don't think what's going on, I don't think a lot of lift-and-shift is going on. Legacy Lawson customers are not moving in droves to the Cloud with their data and all that. There's not a massive lift-and-shift. It's all the new greenfield applications for these new use cases, in terms of predictive analytics. They're being born and living their entire lives in the Cloud. >> And a lot of HR, a lot of HCM, obviously, competing with Workday and Peoplesoft. That stuff's going into the Cloud. We're going to be unpacking this all day today, and tomorrow. Two days here of coverage. >> Indeed, yes indeed. >> Dave: Excited to be here. >> It's going to be a great show. Bruno Mars is performing the final day. >> Jim: Bruno Mars? >> I know, very-- >> You know a company's doing good, Infor, when they can pay for the likes of a Bruno Mars, who's still having mega hits on the radio. I wish I was staying long enough to catch that one. >> I know, indeed, indeed. Well, for Dave and Jim, I'm Rebecca Knight, and we'll be back with more from Inforum 2017 just after this. (fast techno music)