 Live from the Mandalay Convention Center in Las Vegas, Nevada, it's theCUBE at IBM Insight 2014. Here are your hosts, John Furrier and Dave Vellante. Okay, welcome back everyone. We are here live in Las Vegas. This is theCUBE, our flagship program. We go out to the events and instruct the students on noise. I'm John Furrier, the co-founder of Silicon Angle. I mean, my joy, my co-host Dave Vellante, chief analyst at Wikibon.org, also co-founder of Silicon Angle Media. Our next guest is Mark Altschuler, Vice President and Product Management of Business Analytics at IBM. His baby is Watson Analytics, which is the hit of the show. Mark, welcome to theCUBE. Hey, thanks guys. Really appreciate being here and the invitation. So you came to IBM through a startup and you're still hanging around. So they've got some exciting things going. That's a true test, by the way, of company culture. Startups come in, they stay. That's part of IBM's playbook. I mean, you're attracted to great projects. You run product management, which is you're the captain of the ship on the product features. So, what's it like? I mean, Watson is like the product everyone wants. So you feel popular? You got haters out there internally? Customers wanted faster? Come on, nine months, go move faster. Everyone wants it, everyone loves it. It's transformational, it's revolutionary, it's a game changer. And for me, I've spent my whole career in analytics, right out of school, right from implementing solutions, using solutions. As you mentioned, starting my own company around these types of solutions, this is the coolest, most exciting thing I've ever worked on. So Watson, obviously known for jeopardy, the big gimmick in the marketing tactic, but now it's really been operationalized into a product. That's what you do. Big news this week on CNN, BBC, Watson's helping in the Ebola virus. Again, this is the real world application of having really good analytics. Again, those are very sexy, notable, newsworthy items. Take us into the weeds a little bit, so the day-to-day value. The stuff down into the long tail, down into the customer base, into the applications. What is the secret sauce? What's going on? Give us a little feel for some of the day-to-day things going on with Watson. Yeah, so in this specific area, so Watson Analytics, Watson Analytics is in a space we're calling analytical discovery. So we've all seen the last few years data discovery as a very, very popular area. People taking a data set and trying to bring out some nice pretty visualizations based on that. This is saying there's a step in between. In between going from the data to the visualization, which says, what's actually interesting about my data? What are the insights within my data? What should I do because of my data before I'm ready to visualize that data? And we're doing that through predictive technologies that are embedded within Watson Analytics, through cognitive discovery technologies. So it's a really nice compliment throughout this solution in terms of being pretty meaningful to these clients, and it's targeting a persona we haven't targeted with this type of technology previously. So previously, you would take this to a data scientist, right? And we all know there's a shortage of data scientists out there, but you'd really take this type of solution to a data scientist, and you would ask them to bring out the interesting insights within the data that's apparent to them. We're taking this to line of business. Now, I will say it's going to be a mission, and it's going to take us, it's a journey in terms of really getting to the final endpoint of what we want this to be. But initially, we're describing that as someone who's comfortable with data, comfortable working in data, maybe it's a spreadsheet expert. They understand the basics of a relational reporting system, so they understand how to do a restriction or a filter or a subset, and they understand the basics of a relational database, meaning they know how to join two data sets together. If you have that skill set, and we think a lot of people do, this is going to be really exciting technology. So. But if I'm a more senior manager, I don't have that skill set. I can find people with that skill set. Probably, well, let's see. If you're going after a data scientist, they'd still have to find, well, they might have that skill set themselves. They might. Okay, but maybe not. Or they think they do, right? They always have their spreadsheet jockey, and you ask this person, do you have a data scientist? Better, I have the spreadsheet jockey. John over here, John's not a data scientist. John's just good in a spreadsheet, right? So there's definitely a shortage out there in the marketplace in terms of the people that have this very deep analytical skill set, and what's revolutionary about what we're doing is, and I've likened it to those old video games, you know, where you had the level one, who was the beginner user, the level two, the intermediate level three, the advanced. We're using that same paradigm in Watson Analytics. We're saying level one is that business user that's new to advanced analytics, and we want them to get value out of this, but all the way to level three, where we want to be a data scientist accelerator as well. So what do I mean by that? That level one person, they can go in and they can get enough information around whatever it is they're trying to solve. So maybe it's a CRM challenge around what drives win or loss, right? They can go in there and they'll actually be able to see, oh, here's the biggest driver of one or lost. They're going to walk in there with an assumption of, oh, it's the tenure of my reps, that's a big driver, this product's the big driver, but they're always going to have this assumption, and what Watson Analytics does is it challenges that assumption, right? And it actually brings out the deeper relationships that are really driving that product, and when you win with that product, and when you lose with that product. So we're going to give them a lot of interesting insight. High color. Yeah, we're going to give them a lot of interesting insight, but then the analytic journey aspect of it, we have these thumbnails in there where you can get into the data scientists type explorations, we'll tease them into it. If they don't want to go there, they don't have to go there. If they want to drill deeper and deeper and understand the underlying nature of this information, they can. Now, you also are responsible for the BI portfolio, so I'll call it traditional BI, historical, whatever you want to call it, but how does Watson fit into that? So the traditional BI, it's a multifaceted answer. The traditional BI always has a data pump into it, right? It's got a great data pump into it. You've got your data warehouse, it's feeding into traditional BI, it's trusted, it's governed, it's organized, it's normalized, and that makes it really prime for this type of analysis. It's ready to be able to be brought into this type of solution for us to bring out these deeper level of analytical insights. That's one aspect of it. The other aspect of it is, all right, you have these artifacts that you've created in traditional BI, you have these artifacts you create in Watson Analytics, how do you bring them all together? And actually, Watson Analytics does that through a board and tile interface, a technology we brought into Watson called Concert that we released probably about a year and a half ago, and it brings this modern interface with social, with collaboration, with mobile, so any form factor, and it does the boards and the tiles so that you can bring things from traditional BI together with things you've created in Watson Analytics and be able to see them on the same page. So I got to ask you, we were at the Duke World last week, and I think I heard the term data lake 100,000 times. You guys don't really use that term in your marketing anyway. John Furrier rolls his eyes every time he hears it, but customers are starting to use that phraseology. What does it mean to you? Is it a viable concept? We've been trying to get our hands around it, like is this real? Is this just marketing? What's the parlance you use and what does it actually mean in practice? Yeah, it's viable and I guess maybe there's some people that like the term and some people that don't. I'm comfortable with the term, no problem with the term. Very viable, very much part of this architecture in terms of, so what I see is the issue and why these users are going out there and they're buying their own tools for discovery is it's really hard for them to access the data they want to use, right? It's not easy for them to get at it and even when they get at it, it's not easy to prepare that information and get it ready for the type of discovery they want to do. So what do they do? They go out and they grab their own data sets. They probably plug in a whole bunch of assumptions to those data sets as they're working with those data sets. They probably use pretty visualizations to mask the holes that might be within those data sets. They're probably getting out insights that are more informational versus insightful, but that's what they're able to do right now. What we want to do is unlock the value of the billions of dollars that have been invested in these data warehouse across all these organizations and be able to get that information to the users in a more readily accessible way. So I think the data lake is going to facilitate that for us. It allows these users and the paradigm we're using and maybe this closes the bridge for you, the paradigm we're using is a shop for data mentality. So being able to go out there, set up a data marketplace of internal and even external data, but internal data and allowing the users to go and shop for that data in a really easy, organized, pre-formatted way and being able to even see who else is using that data set, how popular is that data set? And we think the data lake concept plays really well into that, exactly. So it's transactional in that model. What's your boss looking at before the meeting that you're going into? Isn't that good? What report did he just read? So who's going to win the World Series? Tell us from Watson. I've got to place a bet down in the sports book before the game starts. Mind you, I'll expose. No, it's not hockey. That's the Bruins, by the way. Canadians, we're anti-monkey all phase. That's a good answer. No, but this is a good point. I want to talk about the go-to market. You guys have done nine months. I'm critical of it because we want it faster. We want Watson now. Ray Wang at Constellation Group is a very high praise of you guys. Amazing effort on nine months to get where you're at. Just the update, I mean, are you guys happy with nine months? I mean, obviously, Steve Mills is like, he wants it faster too. There's a lot of pressure. It's a lot of pressure. It's going really well. The beta's been opening up. We opened up to a number of customers that are here at Insight this week. We've got, going into the conference, I think I heard over 17,000 people signed up for the beta. With all the announcements at the concert, I'm sure we've popped over 20,000 people that have come into the beta. So it's going well. It's opening up right now. We are doing something, again, we're disrupting ourselves, but we're also disrupting the market. I talked about the disrupting ourselves side, but in terms of disrupting the market, we are bringing this out as a freemium model. And that's something that we're not used to or known for, I guess, at IBM. That's the newer concept for us. But we are really looking to get this in the hands of as many people as we can, and we're excited in terms of that type of approach to market. But in terms of going faster, yeah. I mean, the best spot to learn about the value of the tool is get it into the marketplace. Yeah, so beta is obviously an interesting thing. You want to go faster, but also on the product management side, you've probably got a long list of things you want to add to the feature list. So is the beta program with all the backlog and how you work the backlog? Does that have a function of you guys doing some assessment of sequencing the features out? Are you guys looking at it from that standpoint? And if yes, how long is the list and how do we get the beta? So I am the beneficiary of lots of people's hard work and this is one of the disruptive features of Watts Analytics. These technologies aren't new for us. The cognitive discovery, we actually started four years ago and it was in beta by itself to be its own product the last year and its own beta. The predictive data scientists in a box, it got released a year and a half ago into the market as its own product. We're now taking that and bundling this into here. So I've actually got very mature IP and what we're very focused on is the UI, UX harmonization and making it business user consumable. That's the last leg of this journey, but I'm not going out with immature technologies. I actually have a lot of maturity there. So the technology is good. So it's really more of de-risking the MVP, minimum viable product for entering the market. That's kind of where you're looking at. And I would call this an MVP plus. This thing's got lots of bells and whistles. So it's pretty rock solid. And that's the way people should do betas by the way. So you're cobbling together some internal technologies. How fast will we work the backlog on the data? Any, is there a process? I mean, people want to know the magic involved. And this initiative has a small army on it. It's a bigger development team I've ever worked on on any project. So we have resourced this appropriately. The team's working well together. The team is gelling well. In terms of the backlog going in at this point, again, we don't know what we don't know. We'll open up the beta. We're trying to do it in a sensible way. We're phasing the users in and probably 1,000 users kind of each day and kind of trying to get our lessons learned out of that. We've got a good infrastructure in terms of collecting the feedback from them. But it's very out of the box. Like it's not a toolset where you're going in and configuring anything. So there's very few fail points within the software. It's insights. Mark, we really appreciate you taking the time to come on theCUBE. We're excited for you guys. I'll give you the final word. Just bumper sticker Watson analytics. What is it for the folks out there that have been hearing about it? Might want to kick the tires. Give them the bottom line. What does the bumper sticker say about Watson? How would you communicate that? Yeah, it's the step between data and visualization. It's the analytical discovery. It's transformational. It's revolutionary. It's about taking it to a different persona. The business users that hasn't been able to historically get at this type of technology. Watson is the magic behind all the awesomeness around context computing and now cognitive computing, the human piece of it. This is theCUBE. Our part to bring that cognitive data to you, sharing that signal from the noise. This is theCUBE. We'll be right back live in Las Vegas for IBM Insight after this short break.