>>live from Miami, Florida. It's the Q covering. IBM is data in a I forum brought to you by IBM. >>Welcome back to the port of Miami, Everybody. You're watching the Cube, the leader in live tech coverage. We're here covering the IBM data and a I form. Rob Thomas is here. He's the general manager for data in A I and I'd be great to see again. >>Right. Great to see you here in Miami. Beautiful week here on the beach area. It's >>nice. Yeah. This is quite an event. I mean, I had thought it was gonna be, like, roughly 1000 people. It's over. Sold or 17. More than 1700 people here. This is a learning event, right? I mean, people here, they're here to absorb best practice, you know, learn technical hands on presentations. Tell us a little bit more about how this event has evolved. >>It started as a really small training event, like you said, which goes back five years. And what we saw those people, they weren't looking for the normal kind of conference. They wanted to be hands on. They want to build something. They want to come here and leave with something they didn't have when they arrived. So started as a little small builder conference and now somehow continues to grow every year, which were very thankful for. And we continue to kind of expand at sessions. We've had to add hotels this year, so it's really taken off >>you and your title has two of the three superpowers data. And of course, Cloud is the third superpower, which is part of IBMs portfolio. But people want to apply those superpowers, and you use that metaphor in your your keynote today to really transform their business. But you pointed out that only about a eyes only 4 to 10% penetrated within organizations, and you talked about some of the barriers that, but this is a real appetite toe. Learn isn't there. >>There is. Let's go talk about the superpower for a bit. A. I does give employees superpowers because they can do things now. They couldn't do before, but you think about superheroes. They all have an origin story. They always have somewhere where they started and applying a I an organization. It's actually not about doing something completely different. It's about extenuating. What you already d'oh doing something massively better. That's kind of in your DNA already. So we're encouraging all of our clients this week like use the time to understand what you're great at, what your value proposition is. And then how do you use a I to accentuate that? Because your superpower is only gonna last if it's starts with who you are as a company or as a >>person who was your favorite superhero is a kid. Let's see. I was >>kind of into the whole Hall of Justice. Super Superman, that kind of thing. That was probably my cartoon. >>I was a Batman guy. And the reason I love that movie because all the combination of tech, it's kind of reminds me, is what's happening here today. In the marketplace, people are taking data. They're taking a I. They're applying machine intelligence to that data to create new insights, which they couldn't have before. But to your point, there's a There's an issue with the quality of data and and there's a there's a skills gap as well. So let's let's start with the data quality problem described that problem and how are you guys attacking it? >>You're a I is only as good as your data. I'd say that's the fundamental problem and organization we worked with. 80% of the projects get slowed down or they get stopped because the company has a date. A problem. That's why we introduce this idea of the A i ladder, which is all of the steps that a company has to think about for how they get to a level of data maturity that supports a I. So how they collect their data, organize their data, analyze their data and ultimately begin to infuse a I into business processes soap. Every organization needs to climb that ladder, and they're all different spots. So for someone might be, we gotta focus on organization a data catalogue. For others, it might be we got do a better job of data collection data management. That's for every organization to figure out. But you need a methodical approach to how you attack the data problem. >>So I wanna ask you about the Aye aye ladder so you could have these verbs, the verbs overlay on building blocks. I went back to some of my notes in the original Ai ai ladder conversation that you introduced a while back. It was data and information architecture at the at the base and then building on that analytics machine learning. Aye, aye, aye. And then now you've added the verbs, collect, organized, analyze and infused. Should we think of this as a maturity model or building blocks and verbs that you can apply depending on where you are in that maturity model, >>I would think of it as building blocks and the methodology, which is you got to decide. Do wish we focus on our data collection and doing that right? Is that our weakness or is a data organization or is it the sexy stuff? The Aye. Aye. The data science stuff. We just This is just a tool to help organizations organize themselves on what's important. I asked every company I visit. Do you have a date? A strategy? You wouldn't believe the looks you get when you ask that question, you get either. Well, she's got one. He's got one. So we got seven or you get No, we've never had one. Or Hey, we just hired a CDO. So we hope to have one. But we use the eye ladder just as a tool to encourage companies to think about your data strategy >>should do you think in the context I want follow up on that data strategy because you see a lot of tactical data strategies? Well, we use Data Thio for this initiative of that initiative. Maybe in sales or marketing, or maybe in R and D. Increasingly, our organization's developing. And should they develop a holistic data strategy, or should they trying to just get kind of quick wins? What are you seeing in the marketplace? >>It depends on where you are in your maturity cycle. I do think it behooves every company to say We understand where we are and we understand where we want to go. That could be the high level data strategy. What are our focus and priorities gonna be? Once you understand focus and priorities, the best way to get things into production is through a bunch of small experiments to your point. So I don't think it's an either or, but I think it's really valuable tohave an overarching data strategy, and I recommended companies think about a hub and spokes model for this. Have a centralized chief date officer, but your business units also need a cheap date officer. So strategy and one place execution in another. There's a best practice to going about this >>the next you ask the question. What is a I? You get that question a lot, and you said it's about predicting, automating and optimizing. Can we unpack that a little bit? What's behind those three items? >>People? People overreact a hype on topics like II. And they think, Well, I'm not ready for robots or I'm not ready for self driving Vehicles like those Mayor may not happen. Don't know. But a eyes. Let's think more basic it's about can we make better predictions of the business? Every company wants to see a future. They want the proverbial crystal ball. A. I helped you make better predictions. If you have the data to do that, it helps you automate tasks, automate the things that you don't want to do. There's a lot of work that has to happen every day that nobody really wants to do you software to automate that there's about optimization. How do you optimize processes to drive greater productivity? So this is not black magic. This is not some far off thing. We're talking about basics better predictions, better automation, better optimization. >>Now interestingly, use the term black magic because because a lot of a I is black box and IBM is always made a point of we're trying to make a I transparent. You talk a lot about taking the bias out, or at least understanding when bias makes sense. When it doesn't make sense, Talk about the black box problem and how you're addressing. >>That starts with one simple idea. A eyes, not magic. I say that over and over again. This is just computer science. Then you have to look at what are the components inside the proverbial black box. With Watson, we have a few things. We've got tools for clients that want to build their own. Aye, aye, to think of it as a tool box you can choose. Do you want a hammer and you want a screwdriver? You wanna nail you go build your own, aye, aye. Using Watson. We also have applications, so it's basically an end user application that puts a I into practice things like Watson assistant to virtually no create a virtual agent for customer service or Watson Discovery or things like open pages with Watson for governance, risk and compliance. So, aye, aye, for Watson is about tools. You want to build your own applications if you want to consume an application, but we've also got in bed today. I capability so you can pick up Watson and put it inside of any software product in the >>world. He also mentioned that Watson was built with a lot of of of, of open source components, which a lot of people might not know. What's behind Watson. >>85% of the work that happens and Watson today is open source. Most people don't know that it's Python. It's our it's deploying into tensorflow. What we've done, where we focused our efforts, is how do you make a I easier to use? So we've introduced Auto Way. I had to watch the studio, So if you're building models and python, you can use auto. I tow automate things like feature engineering algorithm, selection, the kind of thing that's hard for a lot of data scientists. So we're not trying to create our own language. We're using open source, but then we make that better so that a data scientist could do their job better >>so again come back to a adoption. We talked about three things. Quality, trust and skills. We talked about the data quality piece we talked about the black box, you know, challenge. It's not about skills you mention. There's a 250,000 person Gap data science skills. How is IBM approaching how our customers and IBM approaching closing that gap? >>So think of that. But this in basic economic terms. So we have a supply demand mismatch. Massive demand for data scientists, not enough supply. The way that we address that is twofold. One is we've created a team called Data Science Elite. They've done a lot of work for the clients that were on stage with me, who helped a client get to their first big win with a I. It's that simple. We go in for 4 to 6 weeks. It's an elite team. It's not a long project we're gonna get you do for your success. Second piece is the other way to solve demand and supply mismatch is through automation. So I talked about auto. Aye, aye. But we also do things like using a eye for building data catalogs, metadata creation data matching so making that data prep process automated through A. I can also help that supply demand. Miss Max. The way that you solve this is we put skills on the field, help clients, and we do a lot of automation in software. That's how we can help clients navigate this. So the >>data science elite team. I love that concept because way first picked up on a couple of years ago. At least it's one of the best freebies in the business. But of course you're doing it with the customers that you want to have deeper relationships with, and I'm sure it leads toe follow on business. What are some of the things that you're most proud of from the data science elite team that you might be able to share with us? >>The clients stories are amazing. I talked in the keynote about origin stories, Roll Bank of Scotland, automating 40% of their customer service. Now customer SATs going up 20% because they put their customer service reps on those hardest problems. That's data science, a lead helping them get to a first success. Now they scale it out at Wonderman Thompson on stage, part of big W P p big advertising agency. They're using a I to comb through customer records they're using auto Way I. That's the data science elite team that went in for literally four weeks and gave them the confidence that they could then do this on their own. Once we left, we got countless examples where this team has gone in for very short periods of time. And clients don't talk about this because they have to talk about it cause they're like, we can't believe what this team did. So we're really excited by the >>interesting thing about the RVs example to me, Rob was that you basically applied a I to remove a lot of these mundane tasks that weren't really driving value for the organization. And an R B s was able to shift the skill sets. It's a more strategic areas. We always talk about that, but But I love the example C. Can you talk a little bit more about really, where, where that ship was, What what did they will go from and what did they apply to and how it impacted their businesses? A improvement? I think it was 20% improvement in NPS but >>realizes the inquiry's they had coming in were two categories. There were ones that were really easy. There were when they were really hard and they were spreading those equally among their employees. So what you get is a lot of unhappy customers. And then once they said, we can automate all the easy stuff, we can put all of our people in the hardest things customer sat shot through the roof. Now what is a virtual agent do? Let's decompose that a bit. We have a thing called intent classifications as part of Watson assistant, which is, it's a model that understands customer a tent, and it's trained based on the data from Royal Bank of Scotland. So this model, after 30 days is not very good. After 90 days, it's really good. After 180 days, it's excellent, because at the core of this is we understand the intent of customers engaging with them. We use natural language processing. It really becomes a virtual agent that's done all in software, and you can only do that with things like a I. >>And what is the role of the human element in that? How does it interact with that virtual agent. Is it a Is it sort of unattended agent or is it unattended? What is that like? >>So it's two pieces. So for the easiest stuff no humans needed, we just go do that in software for the harder stuff. We've now given the RVs, customer service agents, superpowers because they've got Watson assistant at their fingertips. The hardest thing for a customer service agent is only finding the right data to solve a problem. Watson Discovery is embedded and Watson assistant so they can basically comb through all the data in the bank to answer a question. So we're giving their employees superpowers. So on one hand, it's augmenting the humans. In another case, we're just automating the stuff the humans don't want to do in the first place. >>I'm gonna shift gears a little bit. Talk about, uh, red hat in open shift. Obviously huge acquisition last year. $34 billion Next chapter, kind of in IBM strategy. A couple of things you're doing with open shift. Watson is now available on open shifts. So that means you're bringing Watson to the data. I want to talk about that and then cloudpack for data also on open shifts. So what has that Red had acquisition done for? You obviously know a lot about M and A but now you're in the position of you've got to take advantage of that. And you are taking advantage of this. So give us an update on what you're doing there. >>So look at the cloud market for a moment. You've got around $600 million of opportunity of traditional I t. On premise, you got another 600 billion. That's public clouds, dedicated clouds. And you got about 400 billion. That's private cloud. So the cloud market is fragmented between public, private and traditional. I t. The opportunity we saw was, if we can help clients integrate across all of those clouds, that's a great opportunity for us. What red at open shift is It's a liberator. It says right. Your application once deployed them anywhere because you build them on red hot, open shift. Now we've brought cloudpack for data. Our data platform on the red hot open shift certified on that Watson now runs on red had open shift. What that means is you could have the best data platform. The best Aye, Aye. And you can run it on Google. Eight of us, Azure, Your own private cloud. You get the best, Aye. Aye. With Watson from IBM and run it in any of those places. So the >>reason why that's so powerful because you're able to bring those capabilities to the data without having to move the date around It was Jennifer showed an example or no, maybe was tail >>whenever he was showing Burt analyzing the data. >>And so the beauty of that is I don't have to move any any data, talk about the importance of not having Thio move that data. And I want I want to understand what the client prerequisite is. They really take advantage of that. This one >>of the greatest inventions out of IBM research in the last 10 years, that hasn't gotten a lot attention, which is data virtualization. Data federation. Traditional federation's been around forever. The issue is it doesn't perform our data virtualization performance 500% faster than anything else in the market. So what Jennifer showed that demo was I'm training a model, and I'm gonna virtualized a data set from Red shift on AWS and on premise repositories a my sequel database. We don't have to move the data. We just virtualized those data sets into cloudpack for data and then we can train the model in one place like this is actually breaking down data silos that exist in every organization. And it's really unique. >>It was a very cool demo because what she did is she was pulling data from different data stores doing joins. It was a health care application, really trying to understand where the bias was peeling the onion, right? You know, it is it is bias, sometimes biases. Okay, you just got to know whether or not it's actionable. And so that was that was very cool without having to move any of the data. What is the prerequisite for clients? What do they have to do to take advantage of this? >>Start using cloudpack for data. We've got something on the Web called cloudpack experiences. Anybody can go try this in less than two minutes. I just say go try it. Because cloudpack for data will just insert right onto any public cloud you're running or in your private cloud environment. You just point to the sources and it will instantly begin to start to create what we call scheme a folding. So a skiing version of the schema from your source writing compact for data. This is like instant access to your data. >>It sounds like magic. OK, last question. One of the big takeaways You want people to leave this event with? >>We are trying to inspire clients to give a I shot. Adoption is 4 to 10% for what is the largest economic opportunity we will ever see in our lives. That's not an acceptable rate of adoption. So we're encouraging everybody Go try things. Don't do one, eh? I experiment. Do Ah, 100. Aye, aye. Experiments in the next year. If you do, 150 of them probably won't work. This is where you have to change the cultural idea. Ask that comes into it, be prepared that half of them are gonna work. But then for the 52 that do work, then you double down. Then you triple down. Everybody will be successful. They I if they had this iterative mindset >>and with cloud it's very inexpensive to actually do those experiments. Rob Thomas. Thanks so much for coming on. The Cuban great to see you. Great to see you. All right, Keep right, everybody. We'll be back with our next guest. Right after this short break, we'll hear from Miami at the IBM A I A data form right back.