 Live from New York, it's theCUBE, covering Inforum 2016, brought to you by Inforum. Now, here are your hosts, Dave Vellante and George Gilbert. We're back, Moham R.F. is here. He's the former CEO of Predictix, company was acquired by Inforum, now an Inforum executive, Moham. Welcome to theCUBE, good to see you. Thank you, good to see you guys. So, exciting times, congratulations on the acquisition. Thank you, we're very excited. The team must be thrilled. We are, yeah. So, there's a lot to do, but when we started talking about this a year ago now, we wanted to take the time and do it in a way where we knew the team would be highly motivated to continue to realize the vision and where everybody on the team had something to look forward to. If you're an enterprise application developer, you're joining the third largest ERP software company on the planet. If you're into the cloud, you're joining, I think, one of the top 10 AWS customers in Infor. If you're into data science, you're joining a company that's created dynamic science labs. If you're into technology, there's obviously a huge opportunity to get your technology deployed, up to, I think, 70,000 enterprise customers. And so, there was a lot that we, to be excited about, but we took the time to make sure that as we did the transaction, people understood that this is really the beginning and that we were going to be doing some awesome things. Something for everybody. And Infor made an investment in prediction. Yes, it started out with an investment. Yeah, we're big believers in what we call do, learn, do. So, the idea of trying something in a limited way to make sure that it feels right, it suits all parties involved. And so, we were very supportive of the idea of Infor getting involved in a minimal way initially to see that the growth numbers that we were experiencing in 2015 were going to continue and improve in 2016. And to see whether we had the right kind of culture and vice versa. So, we grew our recurring revenues by over 40% in 2015. And at the time of the closing of the transaction, we were expecting to grow it by at least 60% this year. That's an attention-getter. And so, take us back to predictix. Why was the company founded? You know, there are a lot of different reasons. Some personal and some based on the business opportunity. Basically, I've been doing enterprise software for 25 years. And I think enterprise software is generally terrible. And I think that this is an opportunity to improve it. We knew nothing better. Now we do. So, we try to rethink it from the perspective of the customer, the retailer. And both technically, with our early bet on the cloud, we launched in 2009 as the first in only true elastic cloud powered retail merchandising and supply chain solution. Our adoption of advanced technologies like machine learning and integer programming and other predictive and prescriptive analytics. But also our business model, the way we like to work with retailers, start small, not get them to spend a half a billion dollars and then maybe see a return in three years, let us sort of do bite-sized chunks as we discussed in the context of our company in Infor. We call it the do-learn-do methodology where we do a little, we deliver, we learn from that experience, then we do some more, then we learn from that experience and we do some more. And you're taking the attitude of you don't want perfection to be the enemy of progress. And as you iterate very, very, very quickly, you get user engagement and you get adoption and you have value. And so it's just trying to rethink it all and thinking through all the weaknesses of the old model, very one-sided risk, very kind of big bang, huge failure rates, a lot of costs ongoing and upfront costs and thinking that there's got to be a much better way of doing it. Really simplifying and de-risking the whole retail supply chain component of enterprise software was sort of the raison d'etre and obviously it worked. You did a little bit and then Now Infor has acquired you. So what's happened like in IBM they call it blue-washing. You get red-washed, I mean it's not smaller company. No, no, I think we want to go in the other direction. I think we want to inject sort of this scrappy DNA into the larger companies. And I'm sure that's welcome. Yeah, I mean. So I think the Infor retail story is a great one. I think a little over a year ago it was one person. Now if you combine us with some of the other acquisitions and you look at the team that's focused purely on retail, we're talking about over 680 people. Okay, so it's probably, if not the fastest growing enterprise startup, it's probably the top three. And the culture around that is very important to keep it sort of agile and fast and fun and all of those things. So it's been kind of the other way around. Like our team, my portion of the team is about 190 people of predictix when it was acquired as 190 people. As part of this transaction, we have 75 Infor retail developers joining our team and then we're getting headcount for another 50 or so to grow further. So think about that. And instead of us being broken apart and distributed within Infor, it's actually we're being kept together as a cohesive unit and me having a whole bunch of Infor people added to the team in the way that, running it in the high growth way that we've been running it. So very excited about that. Earlier we heard that retail, we always know it's been a low margin in business and laggards and technology adoption. SAP and Oracle put a lot of money and effort into it over a period of a very long time. Like in SAP's case, I think already two decades plus. Why do you think they've been unsuccessful and how has your approach changed? You know, I've been doing enterprise software in the retail context for 25 years. In fact, I was at RETEC, which is a company that Oracle bought in 2005, I believe, that became the foundation of the Oracle retail GBU. There are different reasons for different companies but those companies, in my view, can't create a culture that retains the best talent, that creates the right level of entrepreneurialism, that rewards risk-taking, rewards innovation, that cares about the user experience and the customer and are interested in making this a win-win. So like I said, it's a very one-sided way of doing business. And they got to benefit from it because it was them competing with each other and giving the retailer a lesser of two evils choice, okay? And it's been 25 years since anybody's really invested in doing anything new or each of them have solutions that I think go back at least that far. And they've been able to get away with it, right? So if you remember Oracle a few years ago was making fun of the cloud and Larry getting on stage and talking about wires and servers and what's the cloud, right? And we were out there hustling in 2009 as we launched. It was a tough idea to get across but right around 2014, you could sort of feel it, you know, a switch flipped and all of a sudden Larry was, oh, we're only doing cloud and SAP was trying to hustle by acquiring their way into the cloud and it just wasn't, it's just not in their DNA, right? So whether it's the tech or the process or the business model, it just doesn't work in that context. But it's their classic playbook, right? It's FUD and then act like you invented. Yeah. It actually works quite well. You know, the point is you're right on that it's like minimize risk-taking because they don't want to take risks, they want to keep the franchise going. And so I guess the follow-up there is what gives you confidence that you can compete with that large estate. Let's think about this as predictics with 190 people and much lower revenues and infrastructure and for us, we beat them head-to-head competes, okay? Ugly, brutal competes at three of the world's top, three of the US's top six largest retailers, right? Some of them I can talk about like the Home Depot because that's public but the two others I can't talk about and these were hotly, hotly contested wins and they brought the full army of people and we would show up with three or four people and we knew what we were talking about and we offered a much lower risk model and we had the track record and we had the cloud momentum and we had the machine learning momentum. It's devastating when they bring a division of people and three guys can beat them. If you can't beat them, buy them. Yeah. You've told us you have a long history in enterprise software. You probably, you must remember the alliance and then war between ITU technologies and SAP where it turns out that the analytics could be deployed first for the higher return or so customers told ITU and ITU said, so go ahead and do us before SAP. Right. Is the same thing happening with your analytics versus the broader retail transactional systems? Yeah, I think this is a really interesting trend here. So the analytics are the high value solutions, right? There they move the middle solution. With one of our clients, for example, we were able to help them reduce inventory from $5.5 billion worth of inventory down to four. Now we don't take credit for all of that but they would give us credit for at least 150 million of that, okay? And that bulk of that benefit comes from being able to predict, you know what will happen when you're promoting product, for example, more accurately than you were doing before. And that's a really interesting story that I'd love to tell you about because the cloud was a key enabling, cloud and machine learning was key to being able to do that better, right? But you've got to start with the analytics to get the value. You need to release $150 million worth of value in the enterprise that then funds other things. And the cool thing about what we're doing here at Info is we're going in a direction where we're not going to do what SAP and Oracle and others have done historically where you have a layer of technology for the transactional pieces and a layer of transactions for the BI, a technology for the BI pieces and a layer of technology for the planning pieces. The planning solution that you deliver to get the high benefit is the platform that we would use to deliver the execution capability and the transactional capability and so on. So that's very different from the way things worked 25 years ago where you have to have an OLTP layer and an OLAP layer and a BI layer and all that kind of stuff. This is really critical for, potentially for explaining how the software industry is going to unfold. If I repeat what you just said, trying to make sure I'm in my own language, I understand it, you're saying we start with the analytics, we source the data that we need from whatever systems, which are going to be different by customer. And then we have an operational, we have a process for operationalizing it back into the existing transactional system, which again is different from customer to customer. That's correct. And you can then replace the transactional system because the analytics become the interface into the process, right? So if I'm pricing, I don't want an old fashioned form where I'm doing everything by hand. I want an analytical solution with a modern user experience, a smart user experience. And then it stops mattering what is doing the execution and whether it's some legacy piece of software or whether then we just turn that off and replace that with our own execution capability that's built into our analytics, then you're done. Okay, so help me understand how that differs from let's say an Oracle approach or an SAP approach. So Oracle approach might be you sort of filter the data and shove it into exadata perhaps. SAP, the answer is HANA. Right. So talk about those solutions versus your approach and... So it's closer, if I had to say one approach was closer to ours, I would say SAP with HANA because it's the whole rethinking the stack around hybrid transactional analytical processing, okay? And so SAP has made a big bet on HANA and is rewriting everything on HANA and trying to bring transactions and analytics in one place. I think Oracle's story is a lot weaker. Obviously exadata is very interesting technology and the whole sort of engineered stack but it's very much a database story. If you actually look at the reality of how their applications are architected, right? The Oracle applications that they're selling for ERP today still have major components of them written in Oracle forms. They haven't even made the transition to Java, right? Not to mention making the transition to some kind of hybrid technology that has BI and planning built in. So in the Oracle retail world, it's everything maybe might run on exadata but the RMS or the ERP application is very, very different from the BI application which looks, ERP is about what's going in my business today. The BI layer is what happened over the last three or four years and then of course the planning stack is completely different again. And if you add predictive analytics and you add prescriptive analytics, you're talking about five or six different stacks that are not coherent. I mean, I didn't realize that it wasn't written in Java. I thought, you know, I just took Larry to his word. 100% Java. No, it's double-check. So, okay, so but what you've described and what you've accomplished is very difficult because you know, George, I wasn't at Hadoop Summit and all the Hadoop shows that you do but the big data guys struggle. Customers struggle with all the complexity. They struggle with lack of domain expertise, you know, lack of data science capability. Somehow you've put all that together and you've built a very successful business. Why do you think you were able to do that? Cloud was part of it. Maybe you just use the cloud data pipeline and that is simplified things, but. Cloud is just part of it, right? But cloud is a key part of it and another difference is engineering purely for the cloud, right? So imagine, you know, go back to the early 90s since you guys know that era. The people who were trying to be both on Unix, Oracle and on the S400 and on the mainframe and they didn't make it, okay? And even though in the early 90s maybe the mainframe business was bigger than the client server business, right? So what we see here, again, you see it with SAP and Oracle it's kind of like I'm better because I'm both, okay? But let's come back to that example I used earlier about $150 million worth of benefit based on a bit more accurate analytics for a promotional analytics. When we had to compete for that business and we got a shot at doing the work and then so did three or four other very large companies I won't name them, okay? And we all got the same data and we were all asked to make the same predictions, okay? And we, our predictions had at least 25% less error than the next best guy, okay? Why is that? They have engineers, we have engineers, they have a computer scientist, we have computer scientists but they engineered the solutions to work this on premise and on the cloud. We were not constrained by on premise so we can ask Amazon and we do ask Amazon every weekend for 10,000 computing cores on behalf of our customer, 10,000 cores at less than 10 cents a core hour that's less than a thousand bucks for a 10,000 CPU machine, okay? We can then use that monster machine, okay? That normally, in the old days, you had to be the NSA to own, okay? For 10 hours, let's say, to do this very sophisticated machine learning where we very advanced math, very large data sets and then we give that up, okay? So the other guys can't assume that the retailer will buy a 10,000 core machine and have it sit around doing nothing most of the week and therefore they'll engineer the software that has to work on premise for the lowest common denominator and the lowest common denominator might have 16 cores in it, 32 cores, if you really want to kind of go crazy 64 cores, it's a totally different mindset and so if you unshackle yourself and you say, I'm just doing it on the cloud, you can exploit the elasticity of the cloud to do analytics and to do all sorts of things that other guys can't do. I love this conversation because we do a lot of these events and on the one end of the spectrum you got Andy Jassy who said, no, everything's going to the cloud and the other end of the spectrum of all the enterprise guys said, no, it's hybrid, same, same, most of the world's going to be on-prem, you know, et cetera, et cetera, but from a... I love that they think that, let them keep thinking of it. I feel bad actually, I'm giving our secrets away. You know, the old adage, well, you're both right, they can't both be right. In this case, they can't both be right and you look at the momentum that Amazon has and the marginal economics and the efficiencies. Let me give you one more example. I find I'm passionate on this point, I remember the old days, again, we're all old enough to remember Lotus One, Two, Three and how dominant it was on DOS. I mean, you couldn't imagine a world without Lotus One, Two, Three. Everybody who's there. You worked there even. I started, that was my first job out of college. Yeah, completely dominant, you know, Windows comes along and Lotus One, Two, Three actually runs on Windows, okay? It just runs as a character mode app. It doesn't take advantage of the native capability of this new platform, which is a GUI, right? Yeah. Right? And so, taking a client server app and just running it on Amazon, it's kind of like taking Lotus One, Two, Three as a character mode app and having it run on Windows. It still sucks. And it doesn't take advantage of that capability, so. Even worse, when Microsoft started competing with Lotus, with Excel, Lotus decided when Windows first came out, we're not going to support Excel. We're going to support OS Two, we're going to support VMS. We're not going to support Windows, yeah. Oh, it's not Windows. I said, Windows. We're not going to support Windows. We're going to support OS Two. We're winning choices there. VMS, right? And then eventually. MVS, 4,000, that's all. I didn't know about MVS. Yeah, we did. So they felt we had more power as the application. But that perfect example of the marginal economics of volume. Yeah. So you don't make the right platform choice when you try to hedge, right? It's expensive and it's, you fork your effort. You don't know who you are anymore, right? It's going to be interesting to watch that play out. All right, Moham. We'll give you the last word. Inforum 2016, some takeaways for our audience? No, yes, it's week number two, we're very excited to be part of the team. And really excited to be part of Inforum. This is actually my second Inforum. We got to know Infor over a period of time. We had a shared client in Whole Foods. And so really excited about what Charles and Duncan and the team is doing here and we're very, very happy to be part of it. Yeah, Whole Foods up on stage today. Great case study, so. Well, thanks very much again. Congratulations on the acquisition and good luck. Thank you. All right, keep it right there, everybody. We'll be back. To wrap up day one, this is day one of two-day coverage wall-to-wall from the Javits Center, Inforum 2016. Right back.