 Live from the Hilton at Bonnet Creek, Orlando, Florida, extracting the signal from the noise, it's theCUBE, covering Vision 2015, brought to you by IBM. And now your hosts, Dave Vellante and Jeff Frick. Welcome back to theCUBE, everybody. Welcome back to Vision 2015. I'm Dave Vellante with Jeff Frick. Dr. Susara Vanden Hevers here. She runs product management for the decision optimization part of IBM. Susara, welcome to theCUBE. It's great to see you. Thank you. So let's talk about decision optimization. Let's start there. Where does it fit in the IBM portfolio? People, you know, a lot of people know about Cognos and the other products, Watson Analytics now coming in. Where's the decision optimization product fit? What is it? Sure. So decision optimization actually quite recently became part of the analytics team. And so we're very happy that we're finally in our home where we belong. And so if you think of really the main three parts of analytics, so you have the descriptive, which tells you what's going on in your business today, and that's really things like Cognos. Then you have your predictive, which tells you what's going to happen in your business tomorrow or in the future, and that is really the SPSS. And then where we are, we're really that next step of true competitive advantage. So that's what, once you know what's going on, what's gonna happen in the future, what you need to do about it. So we're really that act part. Plan, schedule, decide what you're gonna do. So you're the action part of the portfolio? We're the action part of the portfolio. You like that, okay. So how are organizations using decision optimization today? So it's really used cross industry, completely industry independent. It's a lot useful planning decisions, scheduling, any kind of combinatorial decision where you have a very large number of options you need to consider. And so what we find is, instead of say something like simulation where you might need to run thousands or millions of options, optimization really takes that whole problem in one go and it will just come and suggest to you one or more best solutions in terms of making plans, making schedules. So the software essentially helps me execute on my plan, right? So I get, I make a decision, I manage that decision, say you predict and then you say, okay, we're gonna go. So talk more specifically about what the software does specifically. Does it help me get more efficient? Does it help me just track what I'm doing? Is it nag wear, making sure that people are following up on their promises? Talk a little bit more about that. Right, so I'll use an example to talk about that. So if you think of something like sales and operations planning, you probably have some business goal that you try to achieve. So you might want to think about you want to maximize your revenues or you want to maximize your customer service, maybe improve your customer service levels or you want to minimize your production costs or your storage costs. So all those business goals go into our optimization problems. So that's really the first thing that you need to achieve those business goals. And then you know you have some levers in your business you can pull. So you can produce more of a certain product, less of another one. You might want to be able to change your stock levels. And those are the decisions that we would recommend. If you want to maximize your profit, if you want to focus on customer service, this is what you need to do in terms of changing your product composition, changing your production plans, and then all that within your business constraints. So we capture that reality of what's actually possible in reality. And that becomes your business constraint. How do these products fit with the rest of the portfolio? Because I can see different parts doing different things. Are they tightly integrated, loosely integrated? Maybe talk about that a little. Yeah, it's actually, it goes across the range. There are some products offerings in IBM that we are very tightly integrated with. For example, Maximo, Usersize, Algorithmix, Unica, even SPSS Modeler. We're inside SPSS Modeler. Then there are other products where there might be a slightly looser integration. It might be on a case-by-case basis. So if you think of the IOC, a lot of the smarter cities functionality would use decision optimization inside. And it might be based on a particular user, for example, the water network planning. That's actually an optimization application that started in IBM Research and got integrated into the IOC product. So that's a little bit looser. And then we also go way the other end of the spectrum doing completely custom integrations. So there might be a client that wants a particular set of IBM products together and we can integrate with those. So we have a wide range of APIs. We cover different languages and we basically integrate with anything. And you have some announcements at this event, right? Can we talk about those? Tell us what you're launching here. Yeah, so this week we're actually announcing decision optimization on cloud. So we're taking these decision optimization mathematical solvers. They've been around for about 25 years. They're the market leading solvers for decision optimization used across the world and we've now made them available or we are making them available on cloud. The announcement is this week, it's available in beta. You can go to ibm.biz-try-do-cloud to access it. And it will GA end of the month. What was that URL, ibm? ibm.biz-try-do-cloud. Try-do-cloud. Okay, and then I go there, I get a little trial. Yeah, we have a freemium model. That's right, we have a free trial. Now this product is really targeting our current customer base. So we're really targeting operations, research professionals and developers and those are the people who would be building these applications that can then be integrated with any other visual platform whether it's Cognos or TM1 or whatever the case may be. So how does the cloud change a game for you in terms of go-to-market delivery, everything? Yeah, so there's really two main parts that we see there. The first thing is that optimization which might have traditionally been a little bit of an expensive area to go in for the mid-market because of the cost of the licenses or maybe the cost of the servers you need for the really computationally intensive problems. So that's the one problem we address with cloud. So of course, once you go to cloud that whole cost problem goes away for the mid-market. You can pay per hour, you can access powerful solvers when you need them. If you have a really difficult problem that you need to solve, if you think of retail over Christmas time they're incredibly busy. So then they have these huge problems they need to solve. In the past they would have to have their own servers. Now it just goes to cloud, they use it for that month, that's it. That's all they pay for. If you need it for an hour, you pay for an hour. So that's the one thing. On the other side we have the ease of use. So we believe that putting decision optimization on cloud is really going to change the business landscape. In the past it was really a lot of focus on experts, people with PhDs. We believe this is going to overcome that. And so as a proof of concept for that we've gone and created a tie into Watson Analytics, a proof of concept, not a product. I have to emphasize it's not a product. We cannot sell it, but I will talk about it more this afternoon. And just showing how we can bring optimization to that line of business environment to have people work with optimization but in that context that they love to work with, that very visual, very interactive context. You mentioned developers is one of the targets. Can you talk about that a little bit more? So this is, I presume it's running on software. That's right. This aspect of this, is that the developer that you're going after? Maybe talk some more about that. Yeah, so the job of the developer, so we actually, we have these two personas. There's the operations research expert. That's the guy who creates these models. And he might want to solve these on cloud. So for him we created a drag and drop interface which means if he wants to solve his model on cloud he takes it from his desktop, he drags it over to our website, done. That's it. So some of our competitors require scripts that you need to write or implement, none of that drag and drop. And then his colleague is the IT developer and her job is usually to take his model or application and integrate it into a larger enterprise wide application. And so for her we have what we call the Duke Cloud API. It's a REST API, significance being you can use it with any language. So if she's a Java user she uses that. If she uses Python she uses that, doesn't matter. And basically for her it's about five lines of code to integrate our cloud solver with her application. And so we have all the samples online. She can go and download the samples. We have our developer community and it's just really simplifying that process. Right. But you've also written extensively about how all these new technologies, the social, the cloud, visual tools are enabling much better penetration into a line of business folks that aren't necessarily the people that are building these optimization models. Can you speak to that and really some of the great things that are happening because of that access to more regular kind of line of business folks? Yeah, so that's really where we see the role of what's in analytics and some of the other solutions from IBM, but especially what's in analytics because what we're going to do there is in the past where you might need a very particular model for a very particular business, we're now saying instead, let's just take that 80% model which is generic, we put that inside and we let the user actually interact with the system, interact through that what's in an analytics environment to configure it. So in the past where maybe you needed for 10 solutions, you needed 10 experts. Now you're going to need one and you're going to have the business users interact with it and configure it. Right, right. Yeah, I mean if we take this example, go back to your example of the operations research person and the IT developer in your example, talk about him as the operations researcher. So this is, we would consider him a low code or even no code developer, right? He's going to be dragging and dropping and creating function and business function and mapping to a business process with a drag and drop. Is it truly that simple, right? Yeah, so he's really, he's writing his models with the language he uses, but ultimately to make that solve on cloud, he doesn't have to do anything. In the past he would have had to write some code to make that happen, but he doesn't have to do that. He just drags it over and it's done. Okay, and then if I want some greater customization, I go to the IT developer and she's wearing a hoodie or... Where's the hoodie? She's got a hoodie going on. Okay, so she has access to what the tool sets within Bluemix, is that right? Yeah, exactly. So you can imagine that if you wanted to create an app that also includes optimization. Now you could have that service created in Bluemix. You can combine it, pick and choose other services, whether it's a visualization service, a scoring service, so we know that there's in beta a scoring on cloud from SPSS, and so you can combine and mix and match these things and then you can create your own application and that's actually one of our other target personas is the analytics entrepreneur. So people who really have some knowledge across the board, they know some predictive, they know some optimization, but we make these services available to them much easier to consume and string together to build applications. So one of the things that everybody talks about is this notion of self-service, people trying to create an Amazon-like experience or a Uber-like experience, so what are organizations doing to create that self-service model and how is IBM helping them? So in terms of self-service, well, analytic self-service is really a lot about taking that middleman that IBM support actually out of the picture. So for example, if you want to go and buy our offering today, well, at the end of the month when it's available, you just go online, you put in your credit card and you buy it. There's no more complex process, so we take all that out of the picture. So that's in terms of the buy, but then in terms of the use, the everyday use, there's really that aspect of going to something like Bluemix where there's really, it's almost as though it's a community of developers, of contributors, and they work together. You use your own stuff, you use other people's stuff, and that's really the third aspect, is the community aspect to it. So it's creating the self-service in terms of much less reliant on support calls, filing tickets, really an active community of people contributing and providing support within that community as well. And you had indicated to Sarah that your initial target is the existing customer base. And that's right. And so what do you expect for the cloud uptake there? You sort of indicated there's a surge, example during the holiday season, people might go to the cloud for additional capacity. Are you seeing that in sort of large organizations? Is that mostly mid-sized organizations going to the cloud? Is it sort of mixed? Yeah, there's a bit of a mix. So on the one hand, we see very large corporations who are moving everything to cloud, all their operations. And they want to say, we want to move our optimization to cloud as well. So there are existing customers and they want to just move to cloud. That's part of their corporate strategy. Then we have other customers who might be very happy with their on-prem, but they have the seasonality aspect. So that could be, for example, in retail. Another example is very large sports affiliations. So they do their sports scheduling. Very lumpy. So a lot of them use our software for sports scheduling. And so at certain times of the year, they need to run a lot of problems really fast and they need to get those solutions really fast. And so they would go to the type of bare-medal offering we have where you get the really, really powerful bare-medals servers on SoftLayer and you know you're guaranteed when you submit your problems it's going to be solving instantly. Now for companies who are not so worried about it it has to be instant. There's that virtual machine pool. So there you share with other users and sometimes you might have a little bit of a wait but sometimes most of the time it's just going to solve but if you really want that guaranteed immediate result it's going to be the bare-medal. And then so those are the larger corporations. Then you have the more middle size is really what we see through our business partners. And they're what we call the analytics entrepreneurs. Some of them are actually people from our team who've gone and created their own businesses. And so they are analytics experts and they are creating cloud-based applications for their customers. And so they are the people, they don't want to go and charge their customers a license, a full license just if they use the application a little bit. And so that's really that more middle size. We'll see it now. So they're essentially reselling your offering adding some value to it perhaps. That's right. Did they private label it? Or is it an IBM product to the customer? What is the customer? That would be their own offering. So they would create a custom solution for a particular customer. They can private label, white label that. And then how do you price this? Is it per month kind of thing? Yes, so we have three different pricing layers. So the first one is pay as you go per hour. And basically at the end of the month you'll be billed based on what you've been using. Then the second one is what we call a committed use, committed hours. And all that means is that you need to sign up for a minimum of three months and the more hours you sign up for the less you're gonna pay. So that's just to get that commitment. And then the third one is really the bare metal option. So the bare metal also has a three month minimum commitment. It is a lot more costly but then you get this really dedicated experience of you have this very powerful bare metal machine, you have it when you need it. And that's really for the heavy users of optimization. And are there such things as ELA in this world or not necessarily because enterprise license agreements or is it not so much because not everybody in the organization is gonna use it. What's going on there with enterprise? Yeah, so we've done a lot of that with our on-prem software actually. We do a lot of ELA. And so as we go forward we need to see how that's gonna work. We have often that customers would want to use a mix. So that they might want to keep using what they have on-prem. And then they would add to that some of the cloud functionality, some of the cloud solve. So we're out of time. But last question is we talk to our audience and the folks out there that are candidates for this product, what's the message you want to leave with them? What should they be thinking about in terms of this new product, the whole notion of decision optimization? What would you tell them? Well, first of all, if they don't know what decision optimization is and if they're at this conference, please come to our demo pit. We're at number 46. Or contact me directly. We found that a lot of people at Vision have not yet been exposed to decision optimization because we just joined analytics. We're incredibly powerful, very powerful technique. It's been around since World War II when the US military used it to plan military operations. So we're proven, huge ROI. If you're not looking at us, if you're not talking to us, you're really missing out on what you can do in terms of competitive advantage. So that's what I would say. Pretty straightforward. Well, Sarah, thanks very much for coming on theCUBE. Congratulations on the product announcement. Thank you. And good luck going forward and happy to see you're in the right place in the organization. Yeah, yeah, all right. Well, thank you very much. All right, you're welcome. All right, keep it right there. Everybody, Jeff Frick and I will be back right after this word from IBM Vision. Go to ibmvisiongo.com, check out our digital experience. This is theCUBE, we'll be right back.