 Live from Munich, Germany, it's theCUBE. Covering IBM Fast Track Your Data, brought to you by IBM. Welcome back to Munich, Germany, everybody. This is theCUBE, the leader in live tech coverage. We're covering Fast Track Your Data, IBM's signature moment here in Munich, big themes around GDPR, data science, data science being a team sport. I'm Dave Vellante, I'm here with my co-host, Jim Kobielus. Mark Auschler is here, he's the general manager of IBM Business Analytics. Good to see you again, Mark. Hey, always great to see you. Welcome, it's our first time together. Okay, so we heard your keynote. You were talking about the caveats of correlations. You were talking about rear view mirror analysis versus sort of looking forward, something that I've been sort of harping on for years. You know, I mean, I remember the early days of decision support and the promises of 360 degree views of the customer and predictive analytics, and I've always said it. DSS really never lived up to that, you know. Will big data live up to that? And we're kind of living that now, but what's your take on where we're at in this whole data meme? I mean, look, different customers are at different ends of the spectrum, but people are really getting value. They're becoming these data-driven businesses. I liked what Rob Thomas talked about on stage, right? Visiting companies a few years ago where they'd say, I'm not a technology company. Now, how can you possibly say you're not a technology company, regardless of the industry? Your competitors will beat you if they're using data and you're not. Yeah, and everybody talks about digital transformation, and you hear that a lot in conferences. You guys haven't been pounding that meme other than, you know, below the surface. And to us, digital means data, right? And if you're going to transform digitally, then it's all about the data. You mentioned data-driven. What are you seeing? I mean, most organizations, in our view, aren't data-driven. They're sort of reactive. They're CEOs, maybe want to be data-driven. Maybe they're aboard conversations as to how to get there, but they're mostly focused on how do we keep the lights on? How do we meet our revenue targets? How do we grow a little bit? And then whatever money we have left over, we'll try to, you know, transform. What are you seeing? Is that changing? I would say, look, I can give you an example right from my own space, the software space. For years, we would have product managers offering managers, maybe interviewing clients, on gut feel deciding what features to put, what priority within the next release. Now we have all these products instrumented behind the scenes with data, so we can literally see the friction points, the exit points, how frequently they come back, how long their sessions are. We can even see them effectively graduating within the system where they continue to learn, and where they had shorter sessions, they're now going the longer sessions. That's really, really powerful for us in terms of trying to maximize our outcome from a software perspective. So that's where we kind of like drink our own champagne. I got to ask you, so in around 2003, 2004, HBR had an article, front page cover article of how gut feel beats data and analytics. Now this is 2003, 2004, software development, as you know, it's a lot of art involved. So my question is, how are you doing? Is the data informing you in ways that are non-intuitive and is it driving, you know, business outcomes for IBM? It is, look. You see, I'll see like GMs of sports teams talking about maybe pushing back a little bit on the data, it's not all data driven, there's a little bit of gut, like is the guy going to, is he a checker and hockey or whatever that happens to be. And I would say, when you actually look at what's going on within baseball and you look at the data, when you watch baseball growing up, the commentator might say something along the lines of the pitcher has their stuff. Right, does the pitcher have their stuff or not? Now they literally know the release point based on elevation, IOT within the state of the release point, the spin velocity of the ball, where they mathematically know, does the pitcher have their stuff? Are they hitting their locations? So all that stuff has all become data driven and if you don't want to embrace it, you get beaten. Right, I mean, even in baseball, I remember talking to one of these money ball type guys where I said like, it doesn't weather impact baseball and they're like, yeah, we've looked at it absolutely impacts it because you always hear in football, I remember the old paint manning thing, don't play paint manning in cold weather or don't bet on paint manning in cold weather. So I was like, isn't it the same in baseball? And he's like, absolutely it's the same in baseball. Players perform different based on the climate. Do any managers change their lineup based on that? Never. Yeah. Can we all think of, to speak of HBR, I mean, in the last few years, there was also an article or two by Michael Shragg about the whole notion of real world experimentation and e-commerce driven by data, you know, and in line to an operational process like tuning the design iteratively of say, shopping cart within your e-commerce environment based on the stats on what worked and what does not work. So in many ways, I mean, A-B testing, real world experimentation thrives on data science. Do you see A-B testing becoming a standard business practice everywhere or only in particular industries like the Walmarts of the world? Yeah, so A-B testing, multivariate testing, they're pervasive. Pretty much anyone who has a website ought to be doing this if they're not doing it already. Maybe some startups aren't quite into it. They prioritize in different spots, but mainstream Fortune 500 companies are doing this. The tools have made it really easy. I would say maybe the Achilles heel or the next frontier is that is effectively saying kind of creating one pattern of user and putting everyone in a single bucket, right? Is this button performed better when it's orange or when it's green? Oh, it performs better orange. Really, does it perform well for every segmentation orange better than green or is it just a certain segmentation? So that next kind of frontier is going to be how do we segment it? Know a little bit more about you when you're coming in so that A-B testing starts to build these kind of sub profiles, sub segmentation. And of course the end extreme of that dynamic is one-to-one personalization of experiences and engagements based on knowing 360 degrees about you and what makes you tick as well. So yeah. And add on to that context, right? You have your business, let's even keep it really simple, right? You've got your business life, you've got your social life and your profile of what you're looking for when you're shopping, your social life or something is very different than when you're shopping in your business life. We have to personalize it to the idea where, I don't want to say schizophrenic, but you do have multiple personalities from an online perspective, right? From a digital perspective, it all depends in the moment what is it you're actually doing, right? And what are you doing? Who are you acting for? I want to ask you, your homies, your peeps are the business people. That's where you spend your time. I'm interested in the relationship between those business people and the data science teams. We all hear about how data science unicorns, hard to find, difficult to get the skills. Citizen data science is sort of a nirvana, but how are you seeing businesses bring the domain expertise of the business and blending that with data science? So they do it. I have some cautionary tales that I've experienced in terms of how they're doing it. They feel like, let's just assign the subject matter expert, they'll work with the data scientists, they'll give them context as they're doing their project. But unfortunately, what I've seen time and time again is that subject matter expert right out of the gate brings a tremendous amount of bias based on the types of analysis they've done in the past. That's not how we do it here. Yeah, exactly like, did you test this? Oh yeah, there's no correlation there, we've tried it. Well, just because there's no correlation, as I talked about on stage, doesn't mean it's not part of the pattern in terms of like, you don't want someone in there right off the bat dismissing things. So I always coach when the business user subject matter experts become involved early, they have to be tremendously open minded, not all of them can be. I like bringing them in later because that data scientist, they are unbiased. Like they see this data set, doesn't mean anything to them, they're just numerically telling you what the data set says. Now the business user can then add some context, maybe they grabbed a field that really isn't a relevant field and they can give them that context afterwards. But we just don't want them shutting down kind of roots too early in the process. You know, we've been talking for a couple years now within our community about this digital matrix, this digital fabric that's emerged and you're seeing these horizontal layers of technology, whether it's cloud or security, you all off in with LinkedIn, Facebook and Twitter. There's a data fabric that's emerging and you're seeing all these new business models, whether it's Uber or Airbnb or Waze, et cetera. And then you see this blockbuster announcement last week, Amazon buying Whole Foods. And it's just fascinating to us and it's all about the data that a company like an Amazon can be a content company, it can be a retail company, now it's becoming a grocer. You're seeing Apple get into financial services. So you're seeing industries be able to traverse or companies be able to traverse industries and it's all because of the data. So these conversations absolutely are going on in boardrooms, it's all about the digital transformation, the digital disruption. So how do you see your clients trying to take advantage of that or defend against that? Yeah, look, I mean, you have to be proactive, you have to be willing to disrupt yourself in all these tech industries. It's just moving too quickly. I read a similar story, I think yesterday around potentially blockchain disrupting ride sharing programs, right? Why do you need the intermediary if you have this open ledger and these secure transactions that you can do back and forth with this ecosystem? So there's another interesting disruption. Now do the ride sharing guys proactively get into that and promote it or do they almost in slow motion get replaced by that at some point? So yeah, I think it's incumbent on all of us. Like you don't remain a market leader. Every market leader gets disrupted at some point. The key is do you disrupt yourself and you remain the market leader or do you let someone else disrupt you? And if you get disrupted, how quickly can you recover? Well, you know, you talk to banking executives and they're all talking blockchain. Blockchain is the future and Bitcoin was designed to disintermediate the bank. So there are many, many banks are embracing it. And so it comes back to the data. So my question I have or the discussion I'd like to have is how organizations are valuing data. You can't put data as a value and, you know, asset in your balance sheet. The accounting industry standards don't exist. They probably won't for decades. So how are companies, you know, grokking data value? Is it limiting their ability to move toward a data-driven economy? Limiting, is it a limiting factor that they don't have a good way to value their data and understand how to monetize it? So I have heard of cases where companies have put data on their balance sheet. It's not mainstream at this point, but I mean, you've seen it sometimes in even some bankruptcy proceedings in an industry that's been in bankruptcy protection where they say, hey, but this data asset is really where the value is. It's certainly implicit in valuations. Correct, correct. I mean, you see bios all the time based on the actual data set. So yeah, that data set, they definitely treasure it and they realize that a lot of their answers are within that data set. And they also, I think, understand that there's a lot of peeling the onion that goes on when you're starting to work through that data, right? You have your initial thoughts, then you correct something based on what the data told you to do and then the new data comes in based on what your new experience is and then all of a sudden you have, you see what your next friction point is and you continue to knock down these things. So it is also very iterative working with that data asset. But yeah, these companies are seeing it's very valid when you collect the data. But the other thing is, it's the signal of what's driving your business may not be in your data. More and more often, it may be in market data that's out there. So you think about social media data, you think about weather data and being able to go and grab that information. I remember watching the show Billions where they talked about the hedge fund guys running satellites over like Walmart parking lots to try to predict earnings for the quarter, right? Like you're collecting all this data, but it's out there. Flash boys. Or maybe the value is not so much in the data itself, but what it enables you to develop as a derivative asset, meaning a statistical predictive model or machine learning model that shows the patterns that you can then drive into recommendation engines and your target marketing applications. So you see any clients doing their valuation of data on those derivative assets? Yeah, I see. In lieu of the new business models I see within corporations that have been around for decades is actual data offerings that they make to maybe their ecosystem, their channel. Here's data we have. Here's how you interpret it. We'll continue to collect it. We'll continue to curate it. We'll make it available. And this is really what's driving your business. So yeah, these data assets become something that companies are figuring out how to monetize their data assets. So those derived assets will decay if those models of, for example, machine learning models are not trained with fresh data from the source of stone. And if we're not testing for new variables too, right? Like if the variable was never in the model, you still have to have this discovery process. It's always going on to see what new variables might be out there, what new data set, right? Like if a new IoT sensor in the baseball stadium becomes available, maybe that one I talked about with elevation of the pictorial. Like until you have that, you can't use it. Once you have it, you have to figure out how to use it. All right, let's bring it back to your business. What can I buy from you? What do you sell? What are your products? Yeah, so under me in business analytics is Cognos analytics, Watson analytics, Watson analytics for social media and planning analytics. Cognos is the what, what's going on in my business. Watson analytics is the why. Planning analytics is what do we think is going to happen and we're starting to do more and more smarter what do we think is going to happen based on these predictive models instead of just guessing what's going to happen. And then social media really gets into this idea of trying to find the signal, the sentiment, not just around your own brands. It could be a competitor recall and what now the intent is of that customer. Are they going to now start buying other products or are they going to stick with the recall company? Okay, so the starting point of your business, I mean Cognos, one of the largest acquisitions ever in IBM's history. And of course, it was all about CFOs and reporting and Sarbanes-Oxley was a huge boom to that business. But as I was saying before it, it never really got us to that predictive era. So you're layering those predictive pieces on top of that. That's what you saw on stage. Yes, and that's right, what we saw on stage. And then are you selling to the same constituencies or how is consistency that you sell to changing? Yeah, no, it's actually the same. Well, Cognos BI historically was selling to IT and Cognos Analytics is selling to the business. But if we take that leap forward that we're now in the market we have been for a few years now with Cognos Analytics, yeah, that capability we showed on stage where we talked about not only what's going on, why it's going on, what will happen next and what we ought to do about it. We're selling that capability. For them, the business user, the dashboard becomes like a piece of glass to them and that glass is able to call services that they don't have to be proficient in. They just want to be able to use them. It calls the weather service. It calls the optimization service. It calls the machine learning data science service and it actually gives them information that's forward looking and highly accurate. So they love it because it's cool. They haven't had anything like that before. All right, Mark Ossiola, thanks very much for coming back on theCUBE. It was great to see you. You can't measure hot, as we say in Boston, but you better start measuring. All right, keep right there everybody. Jim and I will be right back after this short break. This is theCUBE, we're live from Fast Tractor Data in Munich. We'll be right back.