>> Live from Boston, Massachusetts, it's The Cube! Covering IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back to The Cube's live coverage of IBM's Chief Data Strategy Summit, I'm your host Rebecca Knight, along with my cohost Dave Vellante. We have a big panel today, these are our social influencers. Starting at the top, we have Christopher Penn, VP Marketing of Shift Communications, then Tripp Braden, Executive Coach and Growth Strategist at Strategic Performance Partners, Mike Tamir, Chief Data Science Officer at TACT, Bob Hayes, President of Business Over Broadway. Thanks so much for joining us. >> Thank you. >> So we're talking about data as a way to engage customers, a way to engage employees. What business functions would you say stand to benefit the most from using data? >> I'll take a whack at that. I don't know if it's the biggest function, but I think the customer experience and customer success. How do you use data to help predict what customers will do, and how do you then use that information to kind of personalize that experience for them and drive up recommendations, retention, upselling, things like that. >> So it's really the customer experience that you're focusing on? >> Yes, and I just released a study. I found that analytical-leading companies tend to use analytics to understand their customers more than say analytical laggards. So those kind of companies who can actually get value from data, they focus their efforts around improving customer loyalty by just gaining a deeper understanding about their customers. >> Chris, you want to jump in here with- >> I was just going to say, as many of us said, we have three things we really care about as business people, right? We want to save money, save time, or make money. So any function that meets those qualifications, is a functional benefit from data. >> I think there's also another interesting dimension to this, when you start to look at the leadership team in the company, now having the ability to anticipate the future. I mean now, we are no longer just looking at static data. We are now looking at anticipatory capability and seeing around corners, so that the person comes to the team, they're bringing something completely different than the team has had in the past. This whole competency of being able to anticipate the future and then take from that, where you take your organization in the future. >> So follow up on that, Tripp, does data now finally trump gut feel? Remember the HBR article of 10, 15 years ago, can't beat gut feel? Is that, we hit a new era now? >> Well, I think we're moving into an era where we have both. I think it's no longer an either or, we have intuition or we have data. Now we have both. The organizations who can leverage both at the same time and develop that capability and earn the trust of the other members by doing that. I see the Chief Data Officer really being a catalyst for organizational change. >> So Dr. Tamir I wonder if I could ask you a question? Maybe the whole panel, but so we've all followed the big data trend and the meme, AI, deep learning, machine learning, same wine, new bottle, or is there something substantive behind it? >> So certainly our capabilities are growing, our capabilities in machine learning, and I think that's part of why now there's this new branding of AI. AI is not what your mother might have thought AI is. It's not robots and cylons and that sort of thing that are going to be able to think intelligently. They just did intelligence tests on the different, like Siri and Alexa, quote AIs from different companies, and they scored horribly. They scored much worse than my, much worse than my very intelligent seven-year old. And that's not a comment on the deficiencies in Alexa or in Siri. It's a comment on these are not actually artificial intelligences. These are just tools that apply machine learning strategically. >> So you are all thinking about data and how it is going to change the future and one of the things you said, Tripp, is that we can now see the future. Talk to me about some of the most exciting things that you're seeing that companies do that are anticipating what customers want. >> Okay, so for example, in the customer success space, a lot of Sass businesses have a monthly subscription, so they're very worried about customer churn. So companies are now leveraging all the user behavior to understand which customers are likely to leave next month, and if they know that, they can reach out to them with maybe some retention campaigns, or even use that data to find out who's most likely to buy more from you in the next month, and then market to those in effective ways. So don't just do a blast for everybody, focus on particular customers, their needs, and try to service them or market to them in a way that resonates with them that increases retention, upselling, and recommendations. >> So they've already seen certain behaviors that show a customer is maybe not going to re-up? >> Exactly, so you just, you throw this data in a machine learning, right. You find the predictors of your outcome that interest you, and then using that information, you say oh, maybe predictors A, B, and C, are the ones that actually drive loyalty behaviors, then you can use that information to segment your customers and market to them appropriately. It's pretty cool stuff. >> February 18th, 2018. >> Okay. >> So we did a study recently just for fun of when people search for the term "Outlook, out of office." Yeah, and you really only search for that term for one reason, you're going on vacation, and you want to figure out how to turn the feature on. So we did a five-year data poll of people, of the search times for that and then inverted it, so when do people search least for that term. That's when they're in the office, and it's the week of February 18th, 2018, will be that time when people like, yep, I'm at the office, I got to work. And knowing that, prediction and data give us specificity, like yeah, we know the first quarter is busy, we know between memorial Day and Labor Day is not as busy in the B to B world. But as a marketer, we need to put specificity, data and predictive analytics gives us specificity. We know what week to send our email campaigns, what week to turn our ad budgets all the way to full, and so on and so forth. If someone's looking for The Cube, when will they be doing that, you know, going forward? That's the power of this stuff, is that specificity. >> They know what we're going to search for before we search for it. (laughter) >> I'd like to know where I'm going to be next week. Why that date? >> That's the date that people least search for the term, "Outlook, out of office." >> Okay. >> So, they're not looking for that feature, which logically means they're in the office. >> Or they're on vacation. (laughter) Right, I'm just saying. >> That brings up a good point on not just, what you're predicting for interactions right now, but also anticipating the trends. So Bob brought up a good point about figuring out when people are churning. There's a flip side to that, which is how do you get your customers to be more engaged? And now we have really an explosion in reinforcement learning in particular, which is a tool for figuring out, not just how to interact with you right now as a one off, statically. But how do I interact with you over time, this week, next week, the week after that? And using reinforcement learning, you can actually do that. This is the the sort-of technique that they used to beat Alpha-Go or to beat humans with Alpha-Go. Machine-learning algorithms, supervised learning, works well when you get that immediate feedback, but if you're playing a game, you don't get that feedback that you're going to win 300 turns from now, right now. You have to create more advanced value functions and ways of anticipating where things are going, this move, so that you see things are on track for winning in 20, 30, 40 moves, down the road. And it's the same thing when you're dealing with customer engagement. You want to, you can make a decision, I'm going to give this customer a coupon that's going to make them spend 50 cents more today, or you can make decisions algorithmically that are going to give them a 50 cent discount this week, next week, and the week after that, that are going to make them become a coffee drinker for life, or customer for life. >> It's about finding those customers for life. >> IBM uses the term cognitive business. We go to these conferences, everybody talks about digital transformation. At the end of the day it's all about how you use data. So my question is, if you think about the bell curve of organizations that you work with, how do they, what's the shape of that curve, part one. And then part two is, where do you see IBM on that curve? >> Well I think a lot of my clients make a living predicting the future, they're insurance companies and financial services. That's where the CDO currently resides and they get a lot of benefit. But one of things we're all talking about, but talking around, is that human element. So now, how do we take the human element and incorporate this into the structure of how we make our decisions? And how do we take this information, and how do we learn to trust that? The one thing I hear from most of the executives I talk to, when they talk about how data is being used in their organizations is the lack of trust. Now, when you have that, and you start to look at the trends that we're dealing with, and we call them data points verses calling them people, now you have a problem, because people become very, almost analytically challenged, right? So how do we get people to start saying, okay, let's look at this from the point of view of, it's not an either or solution in the world we live in today. Cognitive organizations are not going to happen tomorrow morning, even the most progressive organizations are probably five years away from really deploying them completely. But the organizations who take a little bit of an edge, so five, ten percent edge out of there, they now have a really, a different advantage in their markets. And that's what we're talking about, hyper-critical thinking skills. I mean, when you start to say, how do I think like Warren Buffet, how do I start to look and make these kinds of decisions analytically? How do I recreate an artificial intelligence when machine-learning practice, and program that's going to provide that solution for people. And that's where I think organizations that are forward-leaning now are looking and saying, how do I get my people to use these capabilities and ultimately trust the data that they're told. >> So I forget who said it, but it was early on in the big data movement, somebody said that we're further away from a single version of the truth than ever, and it's just going to get worse. So as a data scientist, what say you? >> I'm not familiar with the truth quote, but I think it's very relevant, well very relevant to where we are today. There's almost an arms race of, you hear all the time about automating, putting out fake news, putting out misinformation, and how that can be done using all the technology that we have at our disposal for disbursing that information. The only way that that's going to get solved is also with algorithmic solutions with creating algorithms that are going to be able to detect, is this news, is this something that is trying to attack my emotions and convince me just based on fear, or is this an article that's trying to present actual facts to me and you can do that with machine-learning algorithms. Now we have the technology to do that, algorithmically. >> Better algos than like and share. >> From a technological perspective, to your question about where IBM is, IBM has a ton of stuff that I call AI as a service, essentially where if you're a developer on Bluemix, for example, you can plug in to the different components of Watson at literally pennies per usage, to say I want to do sentiment analysis, I want to do tone analysis, I want personality insights, about this piece, who wrote this piece of content. And to Dr. Tamir's point, this is stuff that, we need these tools to do things like, fingerprint this piece of text. Did the supposed author actually write this? You can tell that, so of all the four magi, we call it, the Microsoft, Amazon, Google, IBM, getting on board, and adding that five or ten percent edge that Tripp was talking about, is easiest with IBM Bluemix. >> Great. >> Well, one of the other parts of this is you start to talk about what we're doing and you start to look at the players that are doing this. They are all organizations that I would not call classical technology organizations. They were 10 years ago, look at a Microsoft. But you look at the leadership of Microsoft today, and they're much more about figuring out what the formula is for success for business, and that's the other place I think we're seeing a transformation occurring, and the early adopters, is they have gone through the first generation, and the pain, you know, of having to have these kinds of things, and now they're moving to that second generation, where they're looking for the gain. And they're looking for people who can bring them capability and have the conversation, and discuss them in ways that they can see the landscape. I mean part of this is if you get caught in the bits and bites, you miss the landscape that you should be seeing in the market, and that's why I think there's a tremendous opportunity for us to really look at multiple markets of the same data. I mean, imagine looking and here's what I see, everyone in this group would have a different opinion in what they're seeing, but now we have the ability to see it five different ways and share that with our executive team and what we're seeing, so we can make better decisions. >> I wonder if we could have a frank conversation, an honest conversation about the data and the data ownership. You heard IBM this morning, saying hey we're going to protect your data, but I'd love you guys, as independents to weigh in. You got this data, you guys are involved with your clients, building models, the data trains the model. I got to believe that that model gets used at a lot of different places, within an industry, like insurance or across retail, whatever it is. So I'm afraid that my data is, my IP is going to seep across the industry. Should I not be worried about that? I wonder if you guys could weigh in. >> Well if you work with a particular vendor, sometimes vendors have a stipulation that we will not share your models with other clients, so you just got to stick to that. But in terms of science, I mean you build a model, right? You want to generalize that to other businesses. >> Right! >> (drowned out by others talking) So maybe if you could work somehow with your existing clients, say here, this is what we want to do, we just want to elevate the waters for everybody, right? So everybody wins when all boats rise, right? So if you can kind of convince your clients that we just want to help the world be better, and function better, make employees happier, customers happier, let's take that approach and just use models in a, that may be generalized to other situations and use them. If if you don't, then you just don't. >> Right, that's your choice. >> It's a choice, it's a choice you have to make. >> As long as you're transparent about it. >> I'm not super worried, I mean, you, Dave, Tripp, and I are all dressed similarly, right? We have the model of shirt and tie so, if I put on your clothes, we wouldn't, but if I were to put on your clothes, it would not be, even though it's the same model, it's just not going to be the same outcome. It's going to look really bad, right, so. Yes, companies can share the models and the general flows and stuff, but there's so much, if a company's doing machine learning well, there's so much feature engineering that's unique to that company that trying to apply that somewhere else, is just going to blow up. >> Yeah, but we could switch ties, like Tripp has got a really cool tie, I'd be using that tie on July 4th. >> This is turning into a different kind of panel (laughter) Chris, Tripp, Mike, and Bob, thanks so much for joining us. This has been a really fun and interesting panel. >> Thank you very much. Thank you. >> Thanks you guys. >> We will have more from the IBM Summit in Boston just after this. (techno music)

>> Announcer: Live from Las Vegas, it's theCUBE covering HPE Discover 2017, brought to you by Hewlett Packard Enterprise. >> Okay, welcome back everyone. We're live here in Las Vegas for SiliconANGLE's CUBE coverage of HPE Discover 2017. This is our seventh year of covering HPE Discover Now. HPE Discover in its second year. I'm John Furrier, my co-host Dave Vellante. We've got two great guests, two doctors, PhD's in the house here. So Eng Lim Goh, VP and SGI CTO, PhD, and Tuomas Sandholm, Professor at Carnegie Mellon University of Computer Science and also runs the marketplace lab over there, welcome to theCube guys, doctors. >> Thank you. >> Thank you. >> So the patient is on the table, it's called machine learning, AI, cloud computing. We're living in a really amazing place. I call it open bar and open source. There's so many new things being contributed to open source, so much new hardware coming on with HPE that there's a lot of innovation happening. So want to get your thoughts first on how you guys are looking at this big trend where all this new software is coming in and these new capabilities, what's the vibe, how do you look at this. You must be, Carnegie Mellon, oh this is an amazing time, thoughts. >> Yeah, it is an amazing time and I'm seeing it both on the academic side and the startup side that you know, you don't have to invest into your own custom hardware. We are using HPE with the Pittsburgh Supercomputing Center in academia, using cloud in the startups. So it really makes entry both for academic research and startups easier, and also the high end on the academic research, you don't have to worry about maintaining and staying up to speed with all of the latest hardware and networking and all that. You know it kind of. >> Focus on your research. >> Focus on the research, focus on the algorithms, focus on the AI, and the rest is taken care of. >> John: Eng talk about the supercomputer world that's now there, if you look at the abundant computer intelligent edge we're seeing genome sequencing done in minutes, the prices are dropping. I mean high performance computing used to be this magical, special thing, that you had to get a lot of money to pay for or access to. Democratization is pretty amazing can I just hear your thoughts on what you see happening. >> Yes, Yes democratization in the traditional HPC approach the goal is to prediction and forecasts. Whether the engine will stay productive, or financial forecasts, whether you should buy or sell or hold, let's use the weather as an example. In traditional HPC for the last 30 years what we do to predict tomorrows weather, what we do first is to write all the equations that models the weather. Measure today's weather and feed that in and then we apply supercomputing power in the hopes that it will predict tomorrows weather faster than tomorrow is coming. So that has been the traditional approach, but things have changed. Two big things changed in the last few years. We got these scientists that think perhaps there is a new way of doing it. Instead of calculating your prediction can you not use data intensive method to do an educated guess at your prediction and this is what you do. Instead of feeding today's weather information into the machine learning system they feed 30 years everyday, 10 thousand days. Everyday they feed the data in, the machine learning system guess at whether it will rain tomorrow. If it gets it wrong, it's okay, it just goes back to the weights that control the inputs and adjust them. Then you take the next day and feed it in again after 10 thousand tries, what started out as a wild guess becomes an educated guess, and this is how the new way of doing data intensive computing is starting to emerge using machine learning. >> Democratization is a theme I threw that out because I think it truly is happening. But let's get specific now, I mean a lot of science has been, well is climate change real, I mean this is something that is in the news. We see that in today's news cycle around climate change things of that as you mentioned weather. So there's other things, there's other financial models there's other in healthcare, in disease and there's new ways to get at things that were kind of hocus pocus maybe some science, some modeling, forecasting. What are you seeing that's right low hanging fruit right now that's going to impact lives? What key things will HPC impact besides weather? Is healthcare there, where is everyone getting excited? >> I think health and safety immediately right. Health and safety, you mentioned gene sequencing, drug designs, and you also mentioned in gene sequencing and drug design there is also safety in designing of automobiles and aircrafts. These methods have been traditionally using simulation, but more and more now they are thinking while these engines for example, are flying can you collect more data so you can predict when this engine will fail. And also predict say, when will the aircraft lands what sort of maintenance you should be applying on the engine without having to spend some time on the ground, which is unproductive time, that time on the ground diagnosing the problems. You start to see application of data intensive methods increased in order to improve safety and health. >> I think that's good and I agree with that. You could also kind of look at some of the technology perspective as to what kind of AI is going to be next and if you look back over the last five to seven years, deep learning has become a very hot part of machine learning and machine learning is part of AI. So that's really lifted that up. But what's next there is not just classification or prediction, but decision making on top of that. So we'll see AI move up the chain to actual decision making on top of just the basic machine learning. So optimization, things like that. Another category is what we call strategic reasoning. Traditionally in games like chess, or checkers and now Go, people have fallen to AI and now we did this in January in poker as well, after 14 years of research. So now we can actually take real strategic reasoning under imperfect information settings and apply it to various settings like business strategy optimization, automated negotiation, certain areas of finance, cyber security, and so forth. >> Go ahead. >> I'd like to interject, so we are very on it and impressed right. If we look back years ago IBM beat the worlds top chess player right. And that was an expert system and more recently Google Alpha Go beat even a more complex game, Go, and beat humans in that. But what the Professor has done recently is develop an even more complex game in a sense that it is incomplete information, it is poker. You don't know the other party's cards, unlike in the board game you would know right. This is very much real life in business negotiation in auctions, you don't quite know what the other party' thinking. So I believe now you are looking at ways I hope right, that poker playing AI software that can handle incomplete information, not knowing the other parties but still able to play expertly and apply that in business. >> I want to double down on that, I know Dave's got a question but I want to just follow this thread through. So the AI, in this case augmented intelligence, not so much artificial, because you're augmenting without the perfect information. It's interesting because one of the debates in the big data world has been, well the streaming of all this data is so high-velocity and so high-volume that we don't know what we're missing. Everyone's been trying to get at the perfect information in the streaming of the data. And this is where the machine learning if I get your point here, can do this meta reasoning or this reasoning on top of it to try to use that and say, hey let's not try to solve the worlds problems and boil the ocean over and understand it all, let's use that as a variable for AI. Did I get that right? >> Kind of, kind of I would say, in that it's not just a technical barrier to getting the big data, it's also kind of a strategic barrier. Companies, even if I could tell you all of my strategic information, I wouldn't want to. So you have to worry not just about not having all the information but are there other guys explicitly hiding information, misrepresenting and vice versa, you doing strategic action as well. Unlike in games like Go or chess, where it's perfect information, you need totally different kinds of algorithms to deal with these imperfect information games, like negotiation or strategic pricing where you have to think about the opponents responses. >> It's your hairy window. >> In advance. >> John: Knowing what you don't know. >> To your point about huge amounts of data we are talking about looking for a needle in a haystack. But when the data gets so big and the needles get so many you end up with a haystack of needles. So you need some augmentation to help you to deal with it. Because the humans would be inundated with the needles themselves. >> So is HPE sort of enabling AI or is AI driving HPC. >> I think it's both. >> Both, yeah. >> Eng: Yeah, that's right, both together. In fact AI is driving HPC because it is a new way of using that supercomputing power. Not just doing computer intensive calculation, but also doing it data intensive AI, machine learning. Then we are also driving AI because our customers are now asking the same questions, how do I transition from a computer intensive approach to a data intensive one also. This is where we come in. >> What are your thoughts on how this affects society, individuals, particularly students coming in. You mentioned Gary Kasparov losing to the IBM supercomputer. But he didn't stop there, he said I'm going to beat the supercomputer, and he got supercomputers and humans together and now holds a contest every year. So everybody talks about the impact of machines replacing humans and that's always happened. But what do you guys see, where's the future of work, of creativity for young people and the future of the economy. What does this all mean? >> You want to go first or second? >> You go ahead first. (Eng and Tuomas laughing) >> They love the fighting. >> This is a fun topic, yeah. There's a lot of worry about AI of course. But I think of AI as a tool, much like a hammer or a saw So It's going to make human lives better and it's already making human lives better. A lot of people don't even understand all the things that already have AI that are helping them out. There's this worry that there's going to be a super species that's AI that's going to take over humans. I don't think so, I don't think there's any demand for a super species of AI. Like a hammer and a saw, a hammer and a saw is better than a hammersaw, so I actually think of AI as better being separate tools for separate applications and that is very important for mankind and also nations and the world in the future. One example is our work on kidney exchange. We run the nationwide kidney exchange for the United Network for Organ Sharing, which saves hundreds of lives. This is an example not only that saves lives and makes better decisions than humans can. >> In terms of kidney candidates, timing, is all of that. >> That's a long story, but basically, when you have willing but incompatible live donors, incompatible with the patient they can swap their donors. Pair A gives to pair B gives to pair C gives to pair A for example. And we also co-invented this idea of chains where an altruist donor creates a while chain through our network and then the question of which combination of cycles and chains is the best solution. >> John: And no manual involvement, your machines take over the heavy lifting? >> It's hard because when the number of possible solutions is bigger than the number of atoms in the universe. So you have to have optimization AI actually make the decisions. So now our AI makes twice a week, these decisions for the country or 66% of the transplant centers in the country, twice a week. >> Dr. Goh would you would you add anything to the societal impact of AI? >> Yes, absolutely on the cross point on the saw and hammer. That's why these AI systems today are very specific. That's why some call them artificial specific intelligence, not general intelligence. Now whether a hundred years from now you take a hundred of these specific intelligence and combine them, whether you get an emergent property of general intelligence, that's something else. But for now, what they do is to help the analyst, the human, the decision maker and more and more you will see that as you train these models it's hard to make a lot of correct decisions. But ultimately there's a difference between a correct decision and, I believe, a right decision. Therefore, there always needs to be a human supervisor there to ultimately make the right decision. Of course, he will listen to the machine learning algorithm suggesting the correct answer, but ultimately the human values have to be applied to decide whether society accepts this decision. >> All models are wrong, some are useful. >> So on this thing there's a two benefits of AI. One is a this saves time, saves effort, which is a labor savings, automation. The other is better decision making. We're seeing the better decision making now become more of an important part instead of just labor savings or what have you. We're seeing that in the kidney exchange and now with strategic reasoning, now for the first time we can do better strategic reasoning than the best humans in imperfect information settings. Now it becomes almost a competitive need. You have to have, what I call, strategic augmentation as a business to be competitive. >> I want to get your final thoughts before we end the segment, this is more of a sharing component. A lot of young folks are coming in to computer science and or related sciences and they don't need to be a computer science major per se, but they have all the benefits of this goodness we're talking about here. Your advice, if both of you could share you opinion and thoughts in reaction to the trend where, the question we get all the time is what should young people be thinking about if they're going to be modeling and simulating a lot of new data scientists are coming in some are more practitioner oriented, some are more hard core. As this evolution of simulations and modeling that we're talking about have scale here changes, what should they know, what should be the best practice be for learning, applying, thoughts. >> For me you know the key thing is be comfortable about using tools. And for that I think the young chaps of the world as they come out of school they are very comfortable with that. So I think I'm actually less worried. It will be a new set of tools these intelligent tools, leverage them. If you look at the entire world as a single system what we need to do is to move our leveraging of tools up to a level where we become an even more productive society rather than worrying, of course we must be worried and then adapt to it, about jobs going to AI. Rather we should move ourselves up to leverage AI to be an even more productive world and then hopefully they will distribute that wealth to the entire human race, becomes more comfortable given the AI. >> Tuomas your thoughts? >> I think that people should be ready to actually for the unknown so you've got to be flexible in your education get the basics right because those basics don't change. You know, math, science, get that stuff solid and then be ready to, instead of thinking about I'm going to be this in my career, you should think about I'm going to be this first and then maybe something else I don't know even. >> John: Don't memorize the test you don't know you're going to take yet, be more adaptive. >> Yes, creativity is very important and adaptability and people should start thinking about that at a young age. >> Doctor thank you so much for sharing your input. What a great world we live in right now. A lot of opportunities a lot of challenges that are opportunities to solve with high performance computing, AI and whatnot. Thanks so much for sharing. This is theCUBE bringing you all the best coverage from HPE Discover. I'm John Furrier with Dave Vellante, we'll be back with more live coverage after this short break. Three days of wall to wall live coverage. We'll be right back. >> Thanks for having us.