 Live from Boston, Massachusetts, it's theCUBE. Covering IBM Chief Data Officer Summit. Brought to you by IBM. Welcome back to theCUBE's live coverage of IBM's Chief Data Strategy Summit. I'm your host, Rebecca Knight, along with my co-host, 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 think a lot of 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 and just gaining a deeper understanding about their customers. Chris, do you want to jump in here? 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. And 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 this, we are no longer just looking at static data, but 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. Does data now finally trump gut feel? You remember the HBR article of 10, 15 years ago? Can't beat gut feel, is that? Are we in a new era now? Well, I think we're moving into an era where you have both. I think it's no longer neither or. If you 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. Tim, 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 sir, 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 xylons and that sort of thing that are going to be able to think intelligently. They just did intelligence tests on the different Siri and Alexa quote, AI is from different companies and they scored horribly. They scored worse than 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 SaaS 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 an effective way. 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 react. Exactly, so you just, you throw this data into 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. 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 volume 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, yeah, I'm at the office, I gotta work. And knowing that the 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 B2B world. But as a marketer, we need 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 cue, when will they be doing that 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. 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. They're on vacation. All 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 of that, which is how do you get your customers to be more engaged? And now we have really an explosion in enforcement 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 sort of technique that they used to beat AlphaGo, or to beat humans with AlphaGo. 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 in ways of anticipating where things are going. This move, so that you see that you're on track for winning, and 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, 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, you know, 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 of their insurance companies, their financial services. That's where the CDO currently resides. And they get a lot of benefit. But one of the 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 versus calling them people, now you'll 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 a neither or solution in the world we live in today. Cognitive organizations are not gonna 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, a five, 10% 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 Buffett? How do I start to look and make these kinds of decisions analytically? How do I recreate an artificial intelligence or machine learning practice and program that's going to solve 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? Not familiar with the truth quote, but I think it's very relevant, very relevant to where we are today with, 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 our disposals for dispersing 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's called AI as a service essentially where if you're a developer on Bloomix, for example, you can plug into 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 of 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 of the four magi we call it, the Microsoft, Amazon, Google, IBM, getting on board and adding that five or 10% edge that Tripp was talking about is easiest with IBM Bloomix. 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, you 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 successful business. And that's the other place I think we're seeing a transformation occurring in the early adopters, is they have gone through the first generation in the pain of having to have these kinds of things. And now they're moving to that second generation where they're looking for the game 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 bytes, you missed the landscape that you should be seeing in the market. And that's where I think there's a tremendous opportunity for us to really look at multiple markets off the same data. I mean, imagine looking and saying, 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, 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 love you guys as independents to weigh in. You got this data, the data, you guys are involved with your clients, building models, the data trains the model. I got to believe that that model gets used in a lot of different places within an industry like insurance or across retail, whatever it is. So I'm afraid that my data is going to, 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 then you just got to stick to that. So, I mean, but in terms of science, I mean, you build a model, right? You want to generalize that to other businesses. Right. So maybe if you could work with, work somehow with your existing clients, say here, this is what we want to do. We just want to elevate the water for everybody, right? So everybody wins when all boats rise, right? So if you can get that, 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 that maybe generalize to other situations and use them. And if you don't, then you just don't. Right, that's your choice. It's a choice. It's always you're transparent about it. Exactly. I'm not super worried. You, Dave, and Tripp and I are all dressed similarly, right? We have the model of, 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 flow 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. They're trying to apply it somewhere else. It's just going to blow up. Yeah, but we could switch ties. That's the first. That's the first. Really cool tie, I've been using that tie in July 4th. This is turning into a different cut. We've got to wait. 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 guys. We will have more from the IBM Summit in Boston just after this.