(upbeat digital music) >> Announcer: Live, from the Javits Center in New York City, it's theCube, covering Inforum 2017. Brought to you by Infor. >> Welcome to day two of theCube's live coverage of Inforum 2017 here in New York City at the Javits Center. I'm your host, Rebecca Knight, along with my co-hosts, Dave Vellante, and Jim Kobielus, who is the lead analyst at Wikibon for AI. So we're here in day two, fellas. We just heard the keynote. Any thoughts on what your expectations are for today, Jim, and what you're hoping to uncover, or at least get more insight on what we learned already in day one? >> I'd like to have Infor unpack a bit more of the Coleman announcement. I wrote a blog last night that I urge our listeners to check out on wikibon.com. There's a number of unanswered issues in terms of their strategy going forward to incorporate Coleman AI and their technology. You know, I suspect that Infor, like most companies, is working out that strategy as they go along, piece by piece, they've got a good framework then. We have Duncan Angove on right after this segment. Dave and I and you, we'll grill Duncan on that and much more, but that in particular. You know, I mean, AI is great. AI is everybody's secret sauce, now. There's a lot of substance behind what they're doing at Infor that sets them apart from their competitors in the ERP space. I want to go deeper there. >> So, yeah, so I'm looking at the blog right now. But what are the particular questions that you have regarding Coleman, in terms of how it's going to work? >> Yeah, well, first of all, I want to know, do they intend to incorporate Coleman AI in their premises-based software offerings? You know, for, I'm sure the vast majority of their customers want to know when, if ever, they're going to get access to Coleman, number one. Number two is, when are they going to complete the process of incorporating Coleman in their CloudSuite portfolio, which is vast and detailed? And then, really number three, are they going to do all the R&D themselves? I mean, they've got AWS as a major partner. AWS has significant intellectual property in AI. Will they call on others to work with them on co-developing these capabilities? You know, those are, like, the high-level things that I want to get out of today. >> Rebecca: Okay, okay. >> Well, so a couple things. So, I mean, the keynote today was okay. It wasn't, like, mind-blowing. We had customer appreciation, which was great. Alexis, who is from Foot Locker, cube alum was up there, and B of A got customer of the year. I met those guys last night at one of the customer appreciation dinners, so that was kind of cool. They all got plaques, or you know, that's nice, little trophies. I heard a lot about design thinking, and they shared some screen shots, essentially, of this new UI, started talking about AI is the new UI. It was very reminiscent of the conversation that we had in May at the ServiceNow Knowledge conference, where they're bringing consumer-like experience to the enterprise. It's always been something that ServiceNow has focused on, and certainly, Charles Phillips and Hook and Loop have been focused on that. The difference is, quite frankly, that ServiceNow showed an actual demo, got a lot of claps as a result. Infor said this is ready to be tested and downloaded, but they didn't show any demo. So that was sort of like, hmm. >> Jim: They haven't shown any demos. >> Rebecca: Yeah. >> Is it really baked out? Steve Lucas was up there. He killed it, very high energy guy. You know, again, another cube alum. He's been in our studio, and he's an awesome dude. >> Jim: He's awesome. >> And I thought he did a really good job. >> From Marketo. >> Talking about, you know, the whole engagement economy, you know, we think it's going a little bit beyond engagement to more action, and systems of an action, I think, is a term you guys use. >> Systems of agency or enablement, yeah. Bringing more of the IoT into it and robotics and so forth, yeah. >> And then DSW was up there. I said yesterday, "I love DSW." I tweeted out that, you know, the CIO had a picture, Ashlee had a picture of DSW, and I said, "Okay, when the girls and I go to DSW, "I break left, they go middle-right, "we meet at the checkout to negotiate "what actually goes home," so that was good. It was kind of fun. And then a lot of talk about digital transformation. Marc Scibelli was talking about that, and IoT and AI and data. So that's sort of, you know, kind of a summary there. As you know, Rebecca, I've been kind of trying to make the math work on the $2-plus billion investment from Koch. >> Rebecca: Yes, this is your-- >> And the messaging that Infor is putting forth is this is a source of new capital for us, but I'm-- >> Rebecca: You're skeptical. >> You know, as a private company, they have the right not to divulge everything, and they're not on a 90-day shot clock. Charles Phillips, I think, said yesterday, "We're on a 10-year shot clock." I said, "Okay." I think what happened is, so I found, I scanned 10-Qs, and I've been doing so for the last couple of days. There is virtually no information about how much, exactly, of the cash went in and what they're doing with it. And so, I suspect, but there are references to Golden Gate Capital and some of the management team taking some money off the table. Cool, that's good. I'm just, it's unclear to me that there's any debt being retired. I think there is none. And it's unclear to me how much cash there is for the business, so the only reference I was able to find, believe it or not, was on Wikipedia, and it says, "Citation still needed," okay? And the number here, and the math works, is $2.68 billion for 66.6% of the company, and a valuation of $10 billion, which Charles Phillips told us off-camera yesterday, it was $10.5 billion. So you can actually make the math work if you take that $10 billion and subtract off the $6 billion in debt. Then the numbers work, and they get five out of 11 board seats, so they've got about 45% or 49%, I think, is the actual number, you know, voting control of the company. So here's the question. What's next? And now, a couple billion for Koch is nothing. It's like the money in my pocket, I mean, it's really-- >> Rebecca: Right, right, right, the empty, yeah, exactly. >> And I suspect what happened is, 'cause it always says "$2 billion plus." So in squinting through this, my guess is, this is a pure guess, we'll try to confirm this, is that what happened is, Koch provided the additional funding to buy Birst recently. That upped their share to 66%, and maybe that's how Koch is going to operate going forward. When they see opportunities to help invest, they're going to do that. Now, one might say, "Well, that's going to further dilute "the existing Infor shareholders," but who cares, as long as the valuation goes up? And that's the new model of private equity. The old model of private equity is suck as much cash out of the company as possible and leave the carcass for somebody else to deal with. The new model of private equity is to invest selectively, use, essentially, what is a zero-interest loan, that $6 billion debt is like free money for Infor, pay down that debt over time with the cashflow of the company, and then raise the valuation of the company, and then at some point, have some kind of public market exit, and everybody's happy and makes a ton of dough. So, I think that's the new private equity play, and I think it's quite brilliant, actually, but there's not a lot of information. So a lot of this, have to be careful, is speculation on my part. >> Right, right. >> Well, the thing is, will the Coleman plan, initiative raise the valuation of the company in the long term if it's, you know, an attrition war in ERP, and they've got SAP, Oracle, Microsoft, all of whom have deep pockets, deeper than Infor, investing heavily in this stuff? Will Coleman be a net-net, just table stays? >> Well, so I think again, there's a couple ways in the tech business, as you guys know, to make money, and one is to invest in R&D and translate that R&D into commercial products. Some companies are really good at that, some companies aren't so good at that. The other way to make money is to do acquisitions and tuck-ins, and many, many companies have built value doing that, certainly Oracle, certainly IBM has, EMC back in the day, with its VMware acquisition, hit probably the biggest home run ever, and Infor has done a very good job of M&A, and I think, clearly, has raised the value of the company. And the other way is to resell technologies and generate cash and keep your costs low. I think a software company like Infor has the opportunity to innovate, to do tuck-in acquisitions, and to drive software marginal economics, so I think, on paper, that's all good, if, to answer your question, they can differentiate. And their differentiation is the way in which they're embedding AI into their deep, vertical, last-mile approach, and that is unique in the software business. Now, the other big question you have is beautiful UIs, and it sounds really great and looks really great, well, when you talk to the customers, they say, "Yeah, it's a little tough to implement sometimes," so it's still ERP, and ERP is complicated, alright? So, you know, it's not like Infor is shielded from some of the complexities of Oracle and SAP. It might look prettier, they might be moving a little faster in certain areas, they might, they clearly have some differentiation. At the end of the day, it's still complicated enterprise software. >> Right, exactly, and we heard that over and over again from the people, from Infor themselves, and also from customers, is that it isn't seamless. It's complicated, it involves a lot of change management initiatives, people have to be on board, and that's not always easy. >> Well, and that's why I'm encouraged, that to see some of the larger SIs, you know, you see Grant Thornton, Capgemini, I think Accenture's here, Deloitte-- >> Rebecca: We're having Capgemini later on the program. >> Deloitte's coming on as well. And so, those guys, even though I always joke they love to eat at the trough and do big, complex things, but, this is maybe not as lucrative as some of the other businesses, but it's clearly a company with momentum, and some tailwind that, in the context of digital transformations and AI, the big SIs and some of the smaller SIs, you know, like Avaap, that we had on yesterday, can do pretty well and actually help companies and customers add value. >> And with a fellow like Charles Phillips at the helm, I mean, he is just an impressive person who, as you have pointed out multiple times, is a real visionary when it comes to this stuff. >> Yeah, except when he's shooting hoops. He's not impressive on the hoop court, no. >> No? Oh! (laughing) >> I tweeted out last night, "He's got Obama's physique, "but not his hoop game." >> Oh! (laughing) >> So don't hate me for saying that, Charles. But yes, I think he's, first of all, he's a software industry guru. I think he, you know, single-handedly changed, I shouldn't say that, single-handedly, but he catalyzed the major change in the software business when Oracle went on its acquisition spree, and he architected that whole thing. It was interesting to hear his comments yesterday about what he sees. He said, "You'll see a lot more tech industry "CEOs running non-tech-industry companies "because they're all becoming SAS companies." >> If they have been so invested in understanding the vertical, they really get it. You can see someone who worked on a retail vertical here going in and being the CEO of Target or Walmart or something. >> Yes, I thought that was a pretty interesting comment from somebody who's got some chops in that business, and again, very impressive, I mean, the acquisitions that this company has done and continues to do. You and I both like the Birst acquisition. It's modern-day BI, it's not sort of just viz, and I don't mean to deposition Clik and Tableau, they've done a great job, you know, but it's not, it doesn't solve all your enterprise-grade, BI sort of problems. And, you know, you talk to the Cognos customer base, as great of an acquisition as that was for IBM, that is a big, chewy, heavy lift that IBM is trying to inject Watson and Watson Analytics. I mean, you know, you used to work at IBM, Jim. And they're doing a pretty good job of that, improving the UI, but it's still big, chunky, Cognos BI. Build cubes, wait for results. >> Yeah. So in many ways, the Birst acquisition for Infor and their portfolio is a bit like the thematics that IBM's been putting out on HTAP, you know, injecting analytics into transactional processing to make them more agile, and so forth. What I like about the Birst acquisition, vis-a-vis Coleman and where Infor is going, is that the Birst acquisition gives them a really good team, the people who really know analytics and how to drive it into transactional environments such as this. They've got, I mean, ostensibly, a deep fund of capital to fund the Coleman development going forward. Plus, they've got a really strong plan. I think there's potential strong differentiators for Infor, far more comprehensive in their plan to incorporate AI across their portfolio than SAP or Oracle or Microsoft have put out there in public, so I think they're in a good position for growth and innovation. >> Well, we have a lot of great guests coming up today. As you said, Duncan Angove is going to be on, up next. So, I'm Rebecca Knight, for Dave Vellante and Jim Kobielus, we will have more from Inforum just after this. (digital music) (pensive electronic music)

(upbeat music) [Announcer] Live from the Wigwam in Pheonix, Arizona, it's the Cube. Covering Data Platforms 2017. Brought to you by Cue Ball. >> Hey welcome back everybody, Jeff Frick here with the Cube. We are coming down to the end of a great day here at the historic Wigwam at the Data Platforms 2017, lot of great big data practitioners talking about the new way to do things, really coining the term data ops, or maybe not coining it but really leveraging it, as a new way to think about data and using data in your business, to be data-driven, software-defined, automated solution and company. So we're excited to have Saket Saurabh, he is the, and I'm sorry I butchered that, Saurabh. >> Saurabh, yeah. >> Saurabh, thank you, sorry. He is the co-founder and CEO of Nexla, and welcome. >> Thank you. >> So what is Nexla, tell us about Nexla for those that aren't familiar with the company. Thank you so much. Yeah so Nexla is a data operations platform. And the way we look at data is that data is increasingly moving between companies and one of the things that is driving that is the growth in machine learning. So imagine you are an e-commerce company, or a healthcare provider. You need to get data from your different partners. You know, suppliers and point-of-sale systems, and brands and all that. And the companies, when they are getting this data, from all these different places, it's so hard to manage. So we think of, you know just like cloud computing, made it easy to manage thousands of servers, we think of data ops as something that makes it easy to manage those thousands of data sources coming from so many partners. So you've jumped straight past the it's a cool buzz term in way to think about things, into the actual platform. So how does that platform fit within the cloud, and on Prim, is it part of the infrastructure, sits next to the infrastructure, is it a conduit? How does that work? >> Yeah, we think of it as, if you think of maybe machine learning or advanced analytics as the application, then data operations is sort of an underlying infrastructure for it. It's not really the hardware, the storage, but it's a layer on top. The job of data operations is to get the data from where it is to where you need it to be, and in the right form and shape. So now you can act on it. >> And do you find yourself replacing legacy stuff, or is this a brand new demand because of all the variant and so many types of datasets that are coming in that people want to leverage. >> Yeah, I mean to be honest, some of this has always been there in the sense that the day you connected a database to a network data started to move around. But if you think of the scale that has happened in the last six or seven years, none of those existing systems were ever designed for that. So when we talk about data growing at at a Moore's Law rate, when we talk about everybody getting into machine learning, when we talk about thousands of data sets across so many different partners that you work with, and when we think that reports that you get from your partners is no more sufficient, you need that underlying data, you can not basically feed that report into an algo. So when you look at all of these things we feel like it is a new thing in some ways. >> Right. Well, I want to unpack that a little bit because you made an interesting comment, before you turned on the cameras you just repeated, that you can't run an algorithm on a report. And in a world where we've got all the shared data sets, and it's funny too right, because you used to run a sample, now you want, you said, the raw. Not only all, but the raw data, so that you can do with it what you wish. Very different paradigm. >> Yeah. >> It sounds like there's a lot more, and you're not just parsing what's in the report, but you have to give it structure that can be combined with other data sources. And that sounds like a rather challenging task. Because the structure, all the metadata, the context that gives the data meaning that is relevant to other data sets, where does that come from? >> Yeah, so what happens, and this has been how technology companies have started to evolve. You want to focus on your core business. And therefore you will use a provider that processes your payments, you will use a provider that gives you search. You will use a provider that provides you the data for example for your e-commerce system. So there are different types of vendors you're working with. Which means that there's different types of data being involved. So when I look at for example a brand today, you could be say, a Nike, and your products are being sold on so many websites. If you want to really analyze your business well, you want data from every single one of those places, where your data team can now access it. So yes, it is that raw data, it is that metadata, and it is the data coming from all the systems that you can look at together and say when I ran this ad this is how people reacted to it, this was the marketing lift from that, this is the purchase that happened across these different channels, this is how my top line or bottom line was affected. And to analyze everything together you need all the data in a place. >> I'm curious on what do you find on the change in the business relationship. Because I'm sure there were agreements structured in another time which weren't quite as detailed, where the expectations in terms of what was exchanged wasn't quite this deep. Are you seeing people have to change their relationships to get this data? Is it out there that they're getting it, or is this really changing the way that people partner in data exchange, on like the example that you just used between say Nike and Foot Locker, to pick a name. >> Yeah, so I think companies that have worked together have always had reports come in, so you would get a daily report of how much you sold. Now just a high-level report of how much you sold is not sufficient anymore. You want to understand where was it bought, in which city, under what weather conditions, by what kind of user and all that stuff. So I think what companies are looking at, again, they have built their data systems, they have the data teams, unless they give the data their teams cannot be effective and you cannot really take a daily sales report and feed that into your algorithm, right? So you need very fine-grained data for that. So I think companies are doing this where, hey you were giving me a report before, I also need some underlying data. Report is for a business executive to look at and see how business is doing, and the underlying data is really for that algorithm to understand and maybe identify things that a report might not. >> Wouldn't there have been already, at least in the example of sell-through, structured data that's been exchanged between partners already like vendor-managed inventory, or you know where like a downstream retailer might make their sell-through data accessible to suppliers who actually take ownership of the inventory and are responsible for stocking it at optimal levels. >> Yeah, I think Walmart was the innovator in that, with the POS link system, back in the day, for retail. But the point is that this need for data to go from one company to their partners and back and forth is across every sector. So you need that in e-commerce, you need that in fintech, we see companies who have to manage your portfolio needs to connect with different banks and brokerages you work with to get the data. We see that in healthcare across different providers and pharmaceutical companies, you need that. We see that in automotive. If every care generates data, an insurance company needs to be able to understand that and look at it. >> This, it's a huge problem you're addressing, because this is the friction between inter-company applications. And we went through this with the B2B marketplaces, 15 plus years ago. But the reason we did these marketplace hubs was so that we could standardize the information exchange. If it's just Walgreens talking to Pfizer, and then doing another one-off deal with, I don't know, Lily, I don't know if they both still exist, it won't work for connecting all of pharmacy with all of pharma. How do you ensure standards between downstream and upstream? >> Yeah. So you're right, this has happened. When we do a wire transfer from one person to another, some data goes from a bank to another bank, still takes hours to get that, it's very tiny amount of data. That has all exploded, we are talking about zetabytes of data now every year. So the challenge is significantly bigger. Now coming to standards, what we have found, that two companies sitting together and defining a standard almost never works. It never works because applications change, systems change, the change is the only constant. So the way we've approached it at our company is, we monitor the data, we sit on top of the data and just learn the structure as we observe data flowing through. So we have tons of data flowing through and we're constantly learning the structure, and are identifying how the structure will map to the destination. So again, applying machine learning to see how the structure is changing, how the data volume is changing. So you are getting data from somewhere say every hour, and then it doesn't show up for two hours. Traditionally systems will go down, you may not even find for five days that the data wasn't there for that. So we look at the data structure, the amount of data, the time when it comes, and everything to instantly learn and be able to inform the downstream systems of what they should be expecting, if there is a change that somebody needs to be alerted about. So a lot of innovation is going in to doing this at scale without necessarily having to predefine something in a tight box that cannot be changed. Because it's extremely hard to control. >> All right, Saket, that's a great explanation. We're going to have to leave it there, we're out of time. And thank you for taking a few minutes out of your day to stop by. >> Thank you. >> All right. Jeff Frick with George Gilbert, we are at Data Platforms 2017, Pheonix Arizona, thanks for watching. (electronic music)