(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)