>> Announcer: Live, from Las Vegas, it's theCUBE. Covering Interconnect 2017. Brought to you by IBM. >> Okay, welcome back everyone we're here live in Las Vegas at the Mandalay Bay, theCUBE's exclusive three day coverage of IBM Interconnect 2017, I'm John Furrier. My co-host, Dave Velliante. Our next guest is Harley Davis, who's the VP of decision management at IBM. Welcome to theCUBE. >> Thank you very much, happy to be here. >> Thanks for your time today, you've got a hot topic, you've got a hot area, making decisions in real-time with data being cognitive, enterprise strong, and data first is really, really hard. So, welcome to theCUBE. What's your thoughts? Because we were talking before we came on about data, we all love, we're all data geeks but the value of the data is all contextual. Give us your color on the data landscape and really the important areas we should shine a light on, that customers are actively working to extract those insights. >> So, you know, traditionally, decisions have really been transactional, all about taking decisions on systems of record, but what's happening now is, we have the availability of all this data, streaming it in real-time, coming from systems of record, data about the past, data about the present, and then data about the future as well, so when you take into account predictive analytics models, machine learning, what you get is kind of data from the future if I can put it that way and what's interesting is how you put it all together, look for situations of risk, opportunity, is there a fraud that's happening now? Is there going to be a lack of resources at a hospital when a patient checks in? How do we put all that context together, look into the future and apply business policies to know what to do about it in real-time and that's really the differentiating use cases that people are excited about now and like you say, it's a real challenge to put that together but it's happening. >> It's happening, and that's, I think that's the key thing and there's a couple megatrends going on right now that's really propelling this. One is machine learning, two is the big data ecosystem as we call it, the big data ecosystem has always been, okay, Hadoop was the first wave, then you saw Spark, and then you're seeing that evolving now to a whole nother level moving data at rest and data in motion is a big conversation, how to do that together, not just I'm a batch only, or real-time only, the integration of those two. Then you combine that with the power of cloud and how fast cloud computing, with compute power, is accelerating, those two forces with machine learning, and IOT, it's just amazing. >> It's all coming together and what's interesting is how you bridge the gap, how you bring it all together, how you create a single system that manages in real-time all this information coming in, how you store it, how you look at, you know, history of events, systems of record and then apply situation detection to it to generate events in real-time. So, you know, one of the things that we've been working on in the decision management lab is a system called decision server insights, which is a big real-time platform, you send a stream of events in, it gets information from systems of records, you insert analytics, predictive analytics, machine learning models into it and then you write a series of situation detection rules that look at all that information and can say right now this is what's happening, I link it in with what's likely to happen in the future, for example I can say my predictive analytics model says based on this data, executed right now, this customer, this transaction is likely, 90% likely to be a fraud and then I can take all the customer information, I can apply my rule and I can apply my business policy to say well what do I do about that? Do I let it go through anyway? Because it's okay, do I reject it? Do I send it to a human analyst? We got to put all that together. >> So that use case that you just described, that's happening today, that's state of the art today, so one of the challenges today, and we all know fraud detection's got much, much better in the last several years, it used to take, if you ever found it, it would take six months, right? And it's too late, but still a lot of false positives, that'll negate a transaction, now that's a business rule decision, right? But are we at the point where even that's going to get better and better and better? >> Well, absolutely. I mean the whole, there have been two main ways to do fraud detection in the past. The first one is kind of long scale predictive analytics that you train every few months and requires, you know, lots and lots of history of data but you don't get new use cases that come up in real-time, like you don't have the Ukrainian hacker who decides, you know, if I do a payment from this one website then I can grab a bunch of money right now and then you have the other alternative, which is having a bunch of human analysts who look for cases like that guy and put it in as business rules and what's interesting is to combine the two, to retrain the models in real-time, and still apply the knowledge that the human analysts can get in real-time, and that's happening every day in lots of companies now. >> And that idea of combining transactional data and analytics, you know, has become popularized over the last couple of years, one obvious use case there is ad-tech, right? Making offers to people, marketing, what's the state of that use case? >> Well, let's look at it from the positive perspective. What we are able to do now is take information about consumers from multiple sources, you can look at the interaction that you've had with them, let's say you're a financial services company, you get all sorts of information about a company, about a customer, sorry, from the CRM system, from the series of interactions you've had with them, from what they've looked at on your website, but you can also get additional information about them if you know them by their Twitter handle or other social media feeds, you can take information from their Twitter feeds, for example, apply some cognitive technology to extract information from that do sentiment analysis, do natural language processing, you get some sense of meaning about the tweets and then you can combine that in real-time in a system like the one I talked about to say ah, this is the moment, right here, where this guy's interested in a new car, we think he just got a promotion or a raise because he's now putting more money into the bank and we see tweets saying "oh I love that new Porsche 911, "can't wait to go look at it in the showroom," if we can put those things together in real-time, why not send him a proactive offer for a loan on a new car, or put him in touch with a dealer? >> No and sometimes as a consumer I want that, you know, when I'm looking for say, scarce tickets to a show or a play-off game or something and I want the best offer and I'm going to five or six different websites, and somebody were to make me an offer, "hey, here are better seats for a lower price," I would be thrilled. >> So geographic information is interesting too for that, so let's say, for example, that you're, you're traveling to Napa Valley and let's say that we can detect that you just, you know, took out some money from the bank, from your ATM in Napa, now we know you're in Napa, now we know that you're a good customer of the bank, and we have a deal with a tour operator, a wine tour operator, so let's spontaneously propose a wine tour to you, give you a discount on that to keep you a good customer. >> Yeah, so relevant offers like that, as a consumer I'd be very interested in. All too often, at least lately, I feel like we're in the first and second innings of that type of, you know, system, where many of the offers that you get are just, wow, okay, for three weeks after I buy the dishwasher, I'm getting dishwasher ads, but it's getting better, you can sort of see it and feel it. >> You can see it getting a little better. I think this is where the combination of all these technologies with machine learning and predictive analytics really comes to the fore and where the new tools that we have available to data scientists, things like, you know, the data scientist experience that IBM offers and other tools, can help you produce a lot more segmented and targeted analytics models that can be combined with all the other information so that when you see that ad, you say oh, the bank really understands me. >> Harley, one of the things that people are working on right now and most customers, your customers and potential customers that we talk to is I got the insights coming, and I'm working on that, and we're pedaling as fast as we can, but I need actionable insight, this is a decision making thing, so decisions are now what people want to do, so that's what you do, so there's some stats out there that decision making can be less than 30 minutes based on good data, the life of the data, as short as six seconds, this speaks to the data in motion, humans aside of it, I might be on my mobile phone, I might be looking at some industrial equipment, whatever, I could be a decision maker in the data center, this is a core problem, what are you guys doing in this area, because this is really a core problem. Or an opportunity. >> Well this all about leveraging, you know, event driven architectures, Kafka, Spark and all the tools that work with it so that we can grab the data in real-time as it comes in, we can associate it with the rest of the context that's relevant for making a decision, so basically with action, when we talk about actionable insights, what are we talking about? We're talking about taking data in real-time, structured, unstructured data, having a framework for managing it, Kafka, Spark, something like decision server insights in ODM, whatever, applying cognitive technology to turn some of the unstructured data into structured data, applying machine learning, predictive analytics, tools like SPSS to create a kind of prediction of what happens in the future and then applying business rules, something like operational decision management, ODM, in order to apply business policies to the insights we've garnered from the rest of the cycle so that we can do something about it, that's decision manager, that's-- >> So you were saying earlier on the use case about, I get some event data, I bring it in to systems of record, I apply some rules to it, I mean, that doesn't sound very hard, I mean, it's almost as if that's happening now-- >> It's hard. >> Well it's hard, let me get, this is my whole point, this is not possible years ago so that's one point, I want to get some color from you on that because this is ungettable, most of the systems, we even go back ten, five years ago, we siloed, so now rule based stuff seems trivial, practically, okay, by some rules, but it's now possible to put this package together and I know it's hard but conceptually those are three concepts that some would say oh, why weren't we doing this before? >> It's been possible for a long time and we have, you know, we have plenty of customers who combine, you know, who do something as simple as when you get approved for a loan, that's based on a score, which is essentially a predictive analytics model combined with business rules that say approve, not approve, ask for more documentations and that's been done for years so it's been possible, what's even more enabled now is doing it in real-time, taking into account a much greater degree of information, having-- >> John: More data sources. >> Data sources, things like social media, things like sensors from IoT, connected car applications, all sorts of things like that and then retraining the models more frequently, so getting better information about the future, faster and faster. >> Give an example of some use cases that you're working with customers on because I think that's fascinating and I think I would agree with you that it's been possible before but the concepts are known, but now it's accelerating to a whole nother level. Talk about some of the use cases end-to-end that you guys have done with customers. >> Let's think about something like an airline, that wants to manage its operations and wants to help its passengers manage operational disruptions or changes. So what we want to do now is, take a series of events coming from all sorts of sources, and that can be basic operational data like the airplanes, what's the airplane, is it running late, is it not running late, is the connection running late, combining it with things about the weather, so information that we get about upcoming weather events from weather analytics models, and then turning that into predicting what's going to happen to this passenger through his journey in the future so that we can proactively notify him that he should be either, we can rebook him automatically on a flight, we can provide him, if we know he's going to be delayed, we can automatically provide him amenities, notify the staff at the airport where he's going to be blocked, because he's our platinum customer, we want to give him lounge access, we want to give him his favorite drink, so combine all this information together and that's a use case-- >> When's this going to happen? >> That's life, that's life. >> I want to fly that airline. Okay, so we've been talking a lot about-- >> Mr. American Airlines? I'm not going to put you on the spot there, hold up, that'll get you in trouble. >> Oh yeah, it's a real life use case. >> And said oh hey, you're not going to make your connection, thanks for letting me know. Okay, so, okay we were talking a lot about the way things used to be, the way things are, and the way things are going to be or actually are today, in that last example, and you talked about event driven workloads. One of the things we've been talking about, at SiliconANGLE and on theCUBE is, is workloads, with batch, interactive, Hadoop brought back batch, and now we have what you call, this event driven workloads, we call it the continuous workloads, right? >> All about data immersion, we all call it different things but it's the same thing. >> Right, and when we look at our forecast, we're like wow, this is really going to hit, it hasn't yet, but it's going to hit the steep part of the s-curve, what do you guys expect in terms of adoption for those types of workloads, is it going to be niche, is it going to be predominant? >> I think it should be predominant and I think companies want it to be predominant. What we still need, I think, is a further iteration on the technology and the ability to bring all these different things together. We have the technologies for the different components, we have machine learning technology, predictive analytics technology, business rules technology, event driven architecture technology, but putting it all together in a single framework, right now it's still a real, it's both a technology implementation challenge, and it's an organizational challenge because you have to have data scientists work with IT architects, work with operational people, work with business policy people and just organizationally, bringing everybody-- >> There's organizational gap. That's what you're talking about. >> Yeah, but every company wants it to happen, because they all see a competitive advantage in doing it this way. >> And what's some of the things that are, barriers being removed as you see them, because that is a consistent thing we're hearing, the products are getting better, but the organizational culture. >> The easy thing is the technology barriers, that's the thing, you know? That's kind of the easy thing to work on, how do we have single frameworks that bring together everything, that let you develop both the machine learning model, the business rules model, and optimization, resource optimization model in a single platform and manage it all together, that's, we're working on that, and that's going to be-- >> I'll throw a wrinkle into the conversation, hopefully a spark, pun intended. Open source and microservices and cloud native apps are coming, that are, with open source, it's actually coming in and fueling a lot more activity. This should be a helpful thing to your point about more data sources, how do you guys talk about that? Because that's something you have to be part of, enabling the inbound migration of new stuff. >> Yeah, we have, I mean, everything's part of the environment. It's been the case for a while that open source has been kind of the driver of a lot of innovation and we assimilate that, we can either assimilate it directly, help our customers use it via services, package it up and rebrand open source technology as services that we manage and we control and integrate it for, on behalf of our customers. >> Alright, last question for you. Future prediction, what's five years out? What's going to happen in your mind's eye, I'm not going to hold you, I mean IBM to this, you personally, just as you see some of this stuff unfolding, machine learning, we're expecting that to crank things up pretty quickly, I'm seeing cognitive, and cognitive to the core, really rocking and rolling here, so what's your, how'd you see the next five years playing out for decision making? >> The first thing is, I don't see Skynet ever happening, I think we're so-- >> Mark Benioff made a nice reference in the keynote about Terminator, I'm like no one pick up on that on Twitter. >> I don't think that's really, nearly impossible, as a scenario but of course what is going to happen and what we're seeing accelerating on a daily basis, is applying machine learning, cognitive technology to more and more aspects of our daily life but I see it, it's in a passive way, so when you're doing image recognition, that's passive, you have to tell the computer tell me what's in this image but you, the human, as the developer or the programmer, still has to kick that off and has to say okay, now that you've told me there's a cat in an image, what do I do about that and that's something a human still has to do and that's, you know, that's the thing that would be scary if our systems started saying we're going to do something on behalf of you because we understand humans completely and what they need so we're going to do it on your behalf, but that's not going to happen. >> So the role of the human is critical, paramount in all this. >> It's not going to go away, we decide what our business policies are and-- >> But isn't, well, autonomous vehicles are an example of that, but it's not a business policy, it's the car making a decision for us, cos we can't react fast enough. >> But the car is not going to tell you where you want to go. If it started, if you get in the car and it said I'm taking you to the doctor because you have a fever, maybe that will happen. (all laugh) >> That's kind of Skynet like. I'd be worried about that. It may make a recommendation. (all laugh) >> Hey, you want to go to the doctor, thank you, no I'm good. >> I really don't see Skynet happening but I do think we're going to get more and more intelligent observations from our systems and that's really cool. >> That's very cool. Harley, thanks so much for coming on theCUBE, sharing the insights, really appreciate it. theCUBE, getting the insights here at IBM Interconnect 2017, I'm John Furrier, stay with us for some more great interviews on day three here, in Las Vegas, more after this short break. (upbeat music)