>>live from Miami, Florida It's the Q covering IBM is data in a I forum brought to you by IBM. >>We're back in Miami and you're watching the cubes coverage of the IBM data and a I forum. Tony hi. Amiss here is a distinguished engineer for Ditch the Digital and Cloud Business Analytics at IBM. Tony, first of all, congratulations on being a distinguished engineer. That doesn't happen often. Thank you for coming on the Cube. Thank you. So your area focus is on the B I and the Enterprise performance management space. >>Um, and >>if I understand it correctly, a big mission of yours is to try to modernize those make himself service, making cloud ready. How's that going? >>It's going really well. I mean, you know, we use things like B. I and enterprise performance management. When you really boil it down, there's that's analysis of data on what do we do with the data this useful that makes a difference in the world, and then this planning and forecasting and budgeting, which everyone has to do whether you are, you know, a single household or whether you're an Amazon or Boeing, which are also some of our clients. So it's interesting that we're going from really enterprise use cases, democratizing it all the way down to single user on the cloud credit card swipe 70 bucks a month >>so that was used to be used to work for Lotus. But Cognos is one of IBM's largest acquisitions in the software space ever. Steve Mills on his team architected complete transformation of IBM is business and really got heavily into it. I think I think it was a $5 billion acquisition. Don't hold me to that, but massive one of the time and it's really paid dividends now when all this sort of 2000 ten's came in and said, Oh, how Duke's gonna kill all the traditional b I traditional btw that didn't happen, that these traditional platforms were a fundamental component of people's data strategies, so that created the imperative to modernize and made sure that there could be things like self service and cloud ready, didn't it? >>Yeah, that's absolutely true. I mean, the work clothes that we run a really sticky were close right when you're doing your reporting, your consolidation or you're planning of your yearly cycle, your budget cycle on these technologies, you don't rip them out so easily. So yes, of course, there's competitive disruption in the space. And of course, cloud creates on opportunity for work loads to be wrong, Cheaper without your own I t people. And, of course, the era of digital software. I find it myself. I tried myself by it without ever talking to a sales person creates a democratization process for these really powerful tools that's never been invented before in that space. >>Now, when I started in the business a long, long time ago, it was called GSS decision support systems, and they at the time they promised a 360 degree view with business That never really happened. You saw a whole new raft of players come in, and then the whole B I and Enterprise Data Warehouse was gonna deliver on that promise. That kind of didn't happen, either. Sarbanes Oxley brought a big wave of of imperative around these systems because compliance became huge. So that was a real tailwind for it. Then her duke was gonna solve all these problems that really didn't happen. And now you've got a I, and it feels like the combination of those systems of record those data warehouse systems, the traditional business intelligence systems and all this new emerging tech together are actually going to be a game changer. I wonder if you could comment on >>well so they can be a game changer, but you're touching on a couple of subjects here that are connected. Right? Number one is obviously the mass of data, right? Cause data has accelerated at a phenomenal pace on then you're talking about how do I then visualize or use that data in a useful manner? And that really drives the use case for a I right? Because A I in and of itself, for augmented intelligence as we as we talk about, is only useful almost when it's invisible to the user cause the user needs to feel like it's doing something for them that super intuitive, a bit like the sort of transition between the electric car on the normal car. That only really happens when the electric car can do what the normal car can do. So with things like Imagine, you bring a you know, how do cluster into a B. I solution and you're looking at that data Well. If I can correlate, for example, time profit cost. Then I can create KP eyes automatically. I can create visualizations. I know which ones you like to see from that. Or I could give you related ones that I can even automatically create dashboards. I've got the intelligence about the data and the knowledge to know what? How you might what? Visualize adversity. You have to manually construct everything >>and a I is also going to when you when you spring. These disparage data sets together, isn't a I also going to give you an indication of the confidence level in those various data set. So, for example, you know, you're you're B I data set might be part of the General ledger. You know of the income statement and and be corporate fact very high confidence level. More sometimes you mention to do some of the unstructured data. Maybe not as high a confidence level. How our customers dealing with that and applying that first of all, is that a sort of accurate premise? And how is that manifesting itself in terms of business? Oh, >>yeah. So it is an accurate premise because in the world in the world of data. There's the known knowns on the unknown knowns, right? No, no's are what you know about your data. What's interesting about really good B I solutions and planning solutions, especially when they're brought together, right, Because planning and analysis naturally go hand in hand from, you know, one user 70 bucks a month to the Enterprise client. So it's things like, What are your key drivers? So this is gonna be the drivers that you know what drives your profit. But when you've got massive amounts of data and you got a I around that, especially if it's a I that's gone ontology around your particular industry, it can start telling you about drivers that you don't know about. And that's really the next step is tell me what are the drivers around things that I don't know. So when I'm exploring the data, I'd like to see a key driver that I never even knew existed. >>So when I talk to customers, I'm doing this for a while. One of the concerns they had a criticisms they had of the traditional systems was just the process is too hard. I got to go toe like a few guys I could go to I gotta line up, you know, submit a request. By the time I get it back, I'm on to something else. I want self serve beyond just reporting. Um, how is a I and IBM changing that dynamic? Can you put thes tools in the hands of users? >>Right. So this is about democratizing the cleverness, right? So if you're a big, broad organization, you can afford to hire a bunch of people to do that stuff. But if you're a startup or an SNB, and that's where the big market opportunity is for us, you know, abilities like and this it would be we're building this into the software already today is I'll bring a spreadsheet. Long spreadsheets. By definition, they're not rows and columns, right? Anyone could take a Roan Collin spreadsheet and turn into a set of data because it looks like a database. But when you've got different tabs on different sets of data that may or may not be obviously relatable to each other, that ai ai ability to be on introspect a spreadsheet and turn into from a planning point of view, cubes, dimensions and rules which turn your spreadsheet now to a three dimensional in memory cube or a planning application. You know, the our ability to go way, way further than you could ever do with that planning process over thousands of people is all possible now because we don't have taken all the hard work, all the lifting workout, >>so that three dimensional in memory Cuba like the sound of that. So there's a performance implication. Absolutely. On end is what else? Accessibility Maw wraps more users. Is that >>well, it's the ability to be out of process water. What if things on huge amounts of data? Imagine you're bowing, right? Howdy, pastors. Boeing How? I don't know. Three trillion. I'm just guessing, right? If you've got three trillion and you need to figure out based on the lady's hurricane report how many parts you need to go ship toe? Where that hurricane reports report is you need to do a water scenario on massive amounts of data in a second or two. So you know that capability requires an old lap solution. However, the rest of the planet other than old people bless him who are very special. People don't know what a laugh is from a pop tart, so democratizing it right to the person who says, I've got a set of data on as I still need to do what if analysis on things and probably at large data cause even if you're a small company with massive amounts of data coming through, people click. String me through your website just for example. You know what if I What if analysis on putting a 5% discount on this product based on previous sales have that going to affect me from a future sales again? I think it's the democratizing as the well is the ability to hit scale. >>You talk about Cloud and analytics, how they've they've come together, what specifically IBM has done to modernize that platform. And I'm interested in what customers are saying. What's the adoption like? >>So So I manage the Global Cloud team. We have night on 1000 clients that are using cloud the cloud implementations of our software growing actually so actually Maur on two and 1/2 1000. If you include the multi tenant version, there's two steps in this process, right when you've got an enterprise software solution, your clients have a certain expectation that your software runs on cloud just the way as it does on premise, which means in practical terms, you have to build a single tenant will manage cloud instance. And that's just the first step, right? Because getting clients to see the value of running the workload on cloud where they don't need people to install it, configure it, update it, troubleshoot it on all that other sort of I t. Stuff that subtracts you from doing running your business value. We duel that for you. But the future really is in multi tenant on how we can get vast, vast scale and also greatly lower costs. But the adoptions been great. Clients love >>it. Can you share any kind of indication? Or is that all confidential or what kind of metrics do you look at it? >>So obviously we look, we look a growth. We look a user adoption, and we look at how busy the service. I mean, let me give you the best way I can give you is a is a number of servers, volume numbers, right. So we have 8000 virtual machines running on soft layer or IBM cloud for our clients business Analytics is actually the largest client for IBM Cloud running those workloads for our clients. So it's, you know, that the adoption has been really super hard on the growth continues. Interestingly enough, I'll give you another factoid. So we just launched last October. Cognos Alex. Multi tenant. So it is truly multi infrastructure. You try, you buy, you give you credit card and away you go. And you would think, because we don't have software sellers out there selling it per se that it might not adopt as much as people are out there selling software. Okay, well, in one year, it's growing 10% month on month cigarette Ally's 10% month on month, and we're nearly 1400 users now without huge amounts of effort on our part. So clearly this market interest in running those softwares and then they're not want Tuesdays easer. Six people pretending some of people have 150 people pretending on a multi tenant software. So I believe that the future is dedicated is the first step to grow confidence that my own premise investments will lift and shift the cloud, but multi tenant will take us a lot >>for him. So that's a proof point of existing customer saying okay, I want to modernize. I'm buying in. Take 1/2 step of the man dedicated. And then obviously multi tenant for scale. And just way more cost efficient. Yes, very much. All right. Um, last question. Show us a little leg. What? What can you tell us about the road map? What gets you excited about the future? >>So I think the future historically, Planning Analytics and Carlos analytics have been separate products, right? And when they came together under the B I logo in about about a year ago, we've been spending a lot of our time bringing them together because, you know, you can fight in the B I space and you can fight in the planning space. And there's a lot of competitors here, not so many here. But when you bring the two things together, the connected value chain is where we really gonna win. But it's not only just doing is the connected value chain it and it could be being being vice because I'm the the former Lotus guy who believes in democratization of technology. Right? But the market showing us when we create a piece of software that starts at 15 bucks for a single user. For the same power mind you write little less less of the capabilities and 70 bucks for a single user. For all of it, people buy it. So I'm in. >>Tony, thanks so much for coming on. The kid was great to have you. Brilliant. Thank you. Keep it right there, everybody. We'll be back with our next guest. You watching the Cube live from the IBM data and a I form in Miami. We'll be right back.

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>We're back in Miami. Welcome everybody. You watching the cube, the leader in live tech coverage. We're here at the IBM data and AI forum. Wow. What a day. 1700 customers. A lot of hands on labs sessions. What used to be the IBM analytics university is sort of morphed into this event. Now you see the buzz is going on. At least the Galean is here. She's the vice president of global sales for IBM data and AI. Welcome to the cube. Thank you for coming on. So this event is buzzing the double from last year almost. And uh, congratulations. >>Well, thank you very much. We have con, uh, lots of countries represented here. We have customers from small to large, every industry represented. And a, it's a, I can see a marked difference in the conversations in just a year around our, how customers want to figure out how to embark on this journey to AI. >>So yeah. So why are they come here? What's the, what's the primary motivation? >>Well, I think one IBM is recognized as the leader in AI and we just came out in the IDC survey as the three time w you know, leader, a recognized leader in AI. And when they come here they know they're going to hear from other clients who have embarked on similar journeys. They know they're going to have access to experts, hands on labs, and we bring our entire IBM team that's focused on data and AI to this event. So it's intimate, it's high skilled, it's high energy and they are learning a ton while they're. >>Yeah, a lot of content and you're educating but you're also trying to inspire people. I mean a raise. I was the hub this morning, he wrote this book, but he's this extreme, extreme, extreme like ultra marathoner. Uh, which I thought was a great talk this morning. And then you did a, I thought a good job of sort of connecting, you know, his talk of anything's possible to now bringing AI into the equation. What are you hearing from customers in terms of what they want to make possible and, and what's that conversation like in the field? >>Well, it's interesting because there is a huge recognition that every client that I talked to you, and they all want to understand this, that they have to be transforming their businesses on this journey to AI. So they all recognize that they need to start. Now. What I find when I talk to clients is that they're all coming in at different entry points. There's a maturity curve. So some are figuring out, you know, how do I move away from just Excel spreadsheets? I'm still running my business on Excel, right? And these are no banks in major that are operating on Excel spreadsheets and they're looking at niche competitors, you know, digital banks that are entering the scene. And if they don't change the way they operate, they're not going to survive. So a lot of companies are coming in knowing that they're low on the maturity curve and they better do something to move up that curve pretty fast. >>Some are in almost the second turn of the crank where they've invested in a lot of the AI technologies, they've built data science platforms, and now they're figuring out how do they get that next rev of productivity improvement? How do they come up with that next business idea that's going to give them that competitive advantage? So what I find is every client is embarking on this journey, which is a big difference where I think we were even a year, 18 months ago, where they were sort of just, okay, this is interesting. Now there I better do something. >>Okay, so you're a resource, you know, as the head of global sales for this group. So when you talk to customers that are immature, if I hear you right, they're saying, help us get started because we're going to fall behind. Uh, we're inefficient right now. We're drowning in spreadsheets, data. Our data quality is not where it needs to be. Help. Where do we start? What do you tell them? >>Well, one, we have a formula that we've proven works with clients. Um, we bring them into our garages where we will do design thinking, architectural workshops, and we figure out a use case because what we try not to do with our clients is boil the ocean. We want them to sh to have something that they can prove success around very quickly, create that minimal viable product, bring it back to the business so that the business can see, Oh, I understand. And then evolve that use case. So we will bring technical specialists, we will bring folks that are our own data scientists to these garage environments and we will work with them on building out this first use case. >>Explain the garage a little bit more. Is that those, those are sort of centers of excellence around the world or how do I tap them as a customer? Is it, is it a freebie? Is it for pay? Isn't it like the data science elite team? How does it all work? >>Well, it is. There are a number of physical locations and it's open to all clients. We have created these with co-leadership from across the entire IBM company. So our services organization, our cloud cognitive organization, all play a role in these garages. So we have a formal structure where a team can engage through a request process into the garages. We will help them define the use case they want to bring into the garage. We will bring them in for a period of time and provide the resources and capabilities and skills and that's not charged to the client. So we're trying to get them started now that they'll take that back to their company and then they will look at follow on opportunities and those may, you know, work out to be different services opportunities as they move forward. But we're on that get started phase. >>Yeah. Yeah. I mean you're a fraud for profit company, so it's great to have a loss leader, but the line outside the door at the garage must be huge for people that want to get in. Hi. How are you managing the dominion? >>Yes, well we're increasing obviously our capacity around the garages. Um, and we're still making customers aware of the garages. So there's still, because it's a commitment on their side, like they just can't come in and kick the tires. We ask them to bring their line of business along with their technical teams into the garages because that's where you get the best product coming out of it. When you know you've got something that's going to solve a business problem, but you have to have buy in from both sides. >>I want to ask you about the AI ladder. You know, Rob Thomas has been using this construct for awhile. It didn't just come out of thin air. I'm sure there was a lot of customer input and a lot of debate about what should be on the ladder. We went, when I first heard of the day AI ladder, it was, there was data in IAA analytics, ML and AI, sort of the building, the technical or technology building blocks. It's now become verbs, which I love, which is collect, organize, analyze and infuse, which is all about operationalizing and scaling. How is that resonating with customers and how do they fit into that methodology or framework? >>Well, I'll tell you, I use that framework with every single client and I described that there is a set of steps and you know, obviously to the ladder that every customer has to embark upon. And it starts with some very basic principles and as soon as you start with the very basic principles, every client is like, of course like it seems so obvious that first and foremost you have to date as the foundation, right? AI is not created out of, you know, someone in a back room. The foundation to AI is, is information and data. Yet every customer, every customer struggles with that data is coming from multiple systems, multiple sources that they can't get to the data fast enough. They're shipping data around an organization. It's not managed. And yet that they know that in five years, the data they think they need today is going to be completely different. >>It could be 12 months, but certainly in the future. So how do you build out that architecture that allows them to build that now, but have the agility to grow as the requirements change? You start with that basic discussion and they're like, well of course. So that's collect and then you bring it up and you talk about how do you govern that data? How do you know where that data originated? Who is the owner? How do you know what that data means? What system did it come from? What's the, you know, who has access to it? How do you create that set of govern data? And we'll of course every client recognizes they have that set of issues. So I could continue working my way up the ladder and every client realizes that, okay, I re I'm, here's where I am today. What you just painted for me is absolutely what I need to focus on and address. Now help me get from a to B. >>So I'm really interested in this discussion because it sounds like you're a very disciplined sales leaders and you said you use the ladder with virtually every client and I presume your sales teams use the ladder. So you train your salespeople how to converse the ladder. And then the other observation I'd love your thoughts on this is every step of the ladder has these questions. So you're asking customers questions and I'm sure it catalyzes conversation, the, the answers to which you have solutions presumably from any of them. But I wonder if you could talk about that. >>Well, let me tell about the ladder and how we're using it with our Salesforce because it was a unifying approach, not just within our own team, our data and AI team, but outside of data and AI. Because not only did we explain it to clients this way, but to the rest of IBM, our business partners, our whole ecosystem. So unifying in that we started every single conversation with our sales team on enabling them on how do they talk to their clients, our materials, our use cases, our references, our marketing campaigns. We tied everything to this unified approach and it's made a huge difference in how we communicate our value to clients and explain this journey to AI in in comprehensive steps that everyone could understand and relate to. >>Love it. How is the portfolio evolving to map into that framework? And what can we expect going forward? What can you share with us at least? >>Well, the other amazing feat I'll call it that we produced around this is I'll talk to a client and I'll describe these capabilities and then I will say to a customer, you don't have to do every one of these things that I've just described, but you can implement what you need when you need it. Because we have built all of this into a unified platform called cloud pack for data and it's a modern data platform. It's built on an open infrastructure built on red hat OpenShift so that you can run it on your own premises as a private cloud or on public clouds, whether that be IBM or Amazon or Zohre. It allows you to have a framework, a platform built on this open modern infrastructure with access to all these capabilities I've just described as services and you decide completely open what services you need to deploy when you grow the platform as you need it. And, Oh, by the way, if you don't have the red hat OpenShift environment set up, we'll package that in a system and I will roll in the system to you and allow you to have access to the capabilities in ours. >>How's the red hat conversation going? I would imagine a lot of the traditional IBM customers are stoked. He just picked up red hat, you know, a very innovative company, open source mindset. Um, at the same time I would imagine a lot of red hat customers saying, is IBM really gonna? Let them keep their culture. How's that conversation going in the field? >>Well, I will tell you we've been a hundred percent consistent in terms of everything that you've heard Jenny and Arvin Krishna talk about in the fact that we are going to maintain their culture, keep them as that separate entity inside of IBM. It's absolutely perpetrated throughout the entire IBM company. Um, we have a lot to learn from, from them as I'm sure they have to learn from us, but it truly is operating and I see it in the clients that I'm working with as a real win-win. >>If you had to take one thing away from this event that you want customers to, to remember, what would it be? >>Start now. Um, because if you don't begin on this journey to AI, you will find yourselves, you know, fighting against new competitors, uh, increasing costs, you know, you have to improve productivity. Every client is embarking on this journey to AI start now. >>And when you were talking about, uh, the maturity model and, and one of those levels was folks that had started already and they wanted to get to the next level, when you go into those clients, do you discern a different sort of attitude? We've started, we're down the path. Did they have more of a spring in their step? Are they like chomping at the bit to really go faster and extend their lead relative to the competition competition? What's the dynamic like in those accounts? >>That's a great question because I was with a client this afternoon, um, a large manufacturer of, uh, of goods and they are at this turning point where they did kind of phase one, they implemented cloud pack for data and they did it to just join some of their disparate systems. Now, I mean, I, I barely got a word in because he was so excited cause he's, now what I'm going to do is I'm going to figure out where my factories should go based on where my products are selling. So he's now looking at how he can change his whole distribution process as a result of getting access to this data and analytics that he never had before. Um, and I was like, okay, well just tell me how I can help you. And he was like, no way ahead. >>So this was the big kickoff day. I know yesterday there was sort of deep learning hands on stuff, the big keynotes. Today we're only here for one day. What are we going to miss? What's, what's happening tomorrow? >>Well, it's a bit of a repeat of today. So we'll have another keynote tomorrow from Beth Smith who runs our Watson, uh, business for IBM. We'll have more hands on labs. We have a lot of customer presentations where they're sharing their best practices. Um, lots of fun. >>Where do you want to see this event go? And what kind of, what's next in an IBM event land? >>Well, the feedback from last year this year says we have to do this again next year. It's, it's, it will be bigger because I think this year approves that it's already doubled and we'll probably see a similar dynamic. Um, so I fully expect us to be here. Well, maybe not here. We're sort of outgrowing this hotel. Um, but doing this event again next year, >>AI machine learning automation, uh, I'll throw in cloud. These are the hottest topics going. Elise, thanks very much for coming to the cube was great to have you. >>It's great. It's great meeting with you. >>It. Thank you for watching everybody. That's a wrap from Miami. Go to siliconangle.com check out all the news of the cube.net is where you'll find all these videos and follow the, uh, the Twitter handles at the cube at the cube three 65. I'm Dave Volante. We're out. We'll see you next time.

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>Welcome back to Miami, everybody. This is the cube, the leader in live tech coverage. We're covering the IBM data and AI forum. Mike Gilfix is here. He's the vice president of digital business automation at IBM. Mike, good to see you again. Good to see you. So your question, what's the difference between a business and an? >>Digital business? Digital business is one that gets digital software scale. So as opposed to traditional business, you know, very manual, very rote. If you want to get software like scale, you need to digitize. >>Okay, that's important. So now, followup question. Uh, you here, I dunno. I think Benioff said every company is a software company or a SAS company. Um, does every company have to be a digital business else? They're toast. >>Uh, I think it's a competitive pressure. I think every business today is looking to get more and more leveraged to stay ahead of their competition. And they're looking to technology to do that. That's actually where we come in because we bring to them automation, technology, automation, technology. They can apply to their business operations that will help them to get that scale. Like you guys got some hard news. Let's get right into it. What do you announce? Sure. So we're announcing a new critical capability. It's part of our cloud pack for automation. It's IBM automation, digital workers. The idea is that you can leverage a digital workforce. You can manage them like people, they can work alongside your people and they can help to free up your people to be that much more productive. They can spend their time on creative things. They can get assistance where they need it all integrated as part of this digital workforce. >>I got a lot of questions, so, so what's a digital worker? Well, it kind of works just like a person does. It can do critical tasks that they need to do, like sift through documents to find out, you know, what to take action on, help with decision making processes, figure out when to act, how to prioritize work. And it can integrate into those people's workflow so they can offload, say, mundane tasks to even more complex tasks where it works alongside them, helps them be more productive. And it sounds a little bit like a software robot, is it? I mean, is it, it is a, it is a form of software robot. You know, the way that we've approached the problem though is we've really approached it from the human aspect. We've looked at the set of things where people spend their time, where they're doing things that they're really not good at. >>For example, we, many organizations actually probably think about even your own job. We spend tons of time sifting through emails, business documents that you're out to turn something to action. It's boring, it's tedious. We're frankly overwhelmed with it. We can use a digital worker to go through those documents, figure out then what to do and then take action on it. Simple example. Um, let's say someone's doing contract analysis. Think about all the time spent going through a contract to figure what's in it, the decision making process. Is it a valid contract? And then determining, you know, who should I get involved when there's a situation so you can bring the right to the right job. >>So is this a a pre-integrated package or do I have to sort of roll my own? How does it, how do I consume it? >>Uh, well that comes as part of our cloud pack, but it comes with a set of tools that you can adapt to your given job roles. So you can describe for example, what's my compliance officer do, what are the set of tasks they do in their day, for example, checking those contracts. And then you can use that to do automation and augmentation where it integrates into the person's workflow and you can manage them just like people. It'll tell you what work they did. And very importantly, we have an element of business controls so that you can trust sort of the work that gets turned over and it'll determine when you have to stage sort of intervention and get a human involved to complete some form of tasks. >>So it sounds like it still sounds a little bit like RPA, but maybe more focused and more specific to certain use cases or tasks. >>Well, if you really look at where RPA is making strides today, it's making strides in data entry and sort of automation of input and data, a lot of back office stuff. But what it doesn't do really well is for example, complex decision making. So consider that compliance officer checking whether something's compliant requires more than simple decision making. It's not excelling today in the area of dealing with unstructured data or figuring out how to integrate into workflows directly. And we've approached this problem from the perspective of the job role. Tell me about the person, not the point thing that I want to get involved. So it's something that can integrate with RPA. It'll extend RPA, but it will really allow you to create a digital worker as in a hybrid workforce management. >>Okay. That's starting to make sense now because you're right, RPA is basically take this, this mundane work process that's very well understood and automated. Sometimes I call it paving the cow path. Um, but, but the, the, to me, the future of RPA is being able to cross that chasm and going into these fuzzy areas that you're describing. Uh, bending into workflows, maybe allowing humans to come into the equation, maybe calling other automations that I can to act on my behalf. >>That's where I think we partner with RPA vendors. We can supply that brain, if you will, that manages the digital worker brain and we can seamlessly integrate it into business processes, many of which actually run on our technology. And so the marriage of those things is effectively really what we've heard clients want. But today struggle to achieve. >>It was interesting because it makes it so you're not trying to replicate RPA, there's not enough vendors out there doing that. You're trying to add value to that in other, I'm sure there are other areas that you can add value to. Um, and are you partnering specifically with RPA vendors? >>We do. We have close partnerships with RPA vendors. Um, you know, one that we've worked very closely with is automation anywhere, but you know, we interoperate and we work with, uh, all the T the the top. >>Okay. So, um, when you think about digital workers, what's the critical issue for customers in terms of enabling >>bullying them? Um, well first a few things. There's a series, there's just a set of trust. You know, if I'm going to turn over work to this digital worker, how do I know that? For example, you know, I don't care what it comes up with. It's not going to sell, uh, inappropriate goods to miners as an example cause it doesn't know, it hasn't been taught those things. So we put some business controls in place that you can specify in natural language so you understand exactly what your digital worker does and it knows then when to get a human involved. Kind of second component is, I think today people want those to be integrated to their workflow. They want to know that it gets you involved, the right person at the right time to take action. And we can integrate that seamlessly into workflow. So that way it's not an isolated thing that just runs as automation. It's truly a synergistic collaboration between both humans and the digital work. >>Great. So what is, what is the cloud pack for automation? We've been talking about the cloud pack for data. What does the cloud pack for automation? >>So it's a set of technologies that digitize what you do in a line of business. So all the technologies in it have a direct analogy to what people do in their workplace. It digitizes your workflow, meaning it coordinates the activities, it digitizes the business data and documents around it and all of the who can see it. Uh, what's the lifecycle? Uh, enables collaboration around those documents. It digitizes decision-making, uh, processing of unstructured data. So really if you think about going to someone who works in a line of business, say they work in supplier onboarding and you ask them what they do, they'll probably describe their day and those kinds of elements and we can digitize that, run it, manage it, and then give you visibility into the. >>How do you, how do you go from what's in the domain experts head to codifying it? Um, is there a, is that a methodology process? Is that services you have tooling to do that? Well, >>yeah. So one of the key ideas behind the technology is it's low code or model driven. So what the thing does is what you see, and that's really important because you can explain to a non technical user essentially what the system is doing so they can check it with you along the way. And we have this methodology that we call playbacks. And the idea is as you kind of elicit requirements from your business user, you put it in the technology at any given point, you can click play, step through your solution, your business user can kind of watch it even if it's incomplete and say, Oh yeah, that's what I had in mind. That isn't what I had in mind. So that's a very powerful technology for doing sort of interactive development between business and it. >>So it's an iterative process where you kind of record the user activity and then show it back, play it back to the user, say, Oh I close. But that's what make this alteration. >>And once you've digitized your operations on it, the automation play is you can integrate things like digital workers or we actually allow you to use the data from your operations to find ways to scale your workforce. Well >>IBM is obviously in addition to a technology company, you're world-class services organization. Um, one of the largest and in, in most capable SIS in the world, global scale with a lot of domain expertise by it, pick an industry, health care, manufacturing, financial services, name it, IBM's got domain expertise there are able to tap that deep domain expertise to drive your business. >>Sure. So first I'm in the software part of IBM. So I support a broad ecosystem, which is inclusive of partners, specific to IBM global services. We actually have an IBM automation practice that, uh, has expertise specifically in the area of how to apply automation technology to business operations. >>Okay. So I love that answer cause basically I'll translate, you said I'm an arms dealer, I'll sell software to my, my colleagues within IBM, but I love all my partners just as well a little more benign than. Yes. Nonetheless. But the point is your, your, your, your, your goal is to scale your software across, uh, many as clients as possible. If they want to use a competitor of IBM global services and that's fine with you, obviously they can yet. Yeah. And so what's that ecosystem look like? I mean, you've got it as a software company, you've got to develop that ecosystem. >>Yeah, we have a massive business partner ecosystem. Um, everything from larger size of course, as you mentioned, but we have lots of regional size. We have a lot of people that have created vertical solutions around our technology. In fact, that's one of the key ways in which we go to market where they've developed something that's specific say to accounts payable or loan processing or you know, health care claims and so on. And so that allows us to extend our reach to niches and it gives them an opportunity to add value add. >>Okay. So I'm going to ask you some thoughts on automation in general. We've seen a lot of text bending, uh, but we haven't seen a productivity boost as a result in the last several years except for this I guess first quarter of 2019, which is the latest data we saw a big uptick. And so a lot of people think we're, we're on the cusp of a productivity boom. Um, that obviously is your business. What are your thoughts? What's your point of view on all that? >>Well, so, uh, we think of ourselves as our mission in life is to bring digital scale to knowledge workers. And let me explain why that's so important. Um, this industry has been talking about digital transformation for a really long time and we've actually been quite successful in digitizing. We're not done, but we've been quite successful in digitizing a huge portion of our business. But the side effect of digitization is that we generated all of this work. You expect that digital business to serve twice as many customers or be that much more responsive and people can't keep up. And what it's done is it's fueled this growth of knowledge work where today we're not doing manual things the way that they were before they were digitized. But instead we're doing them in software. So how do we help people to keep pace? And that's the goal of automation technology. And there's this explosion of knowledge work. To some extent, organizations far and wide are figuring out, okay, how do I get productivity in this new era? That's where we come in. We can help them get that productivity. And we really are in the cusp of people using those techniques now to get that next level of productivity. >>So, and so I've been saying for a while that I feel like there's this huge wave coming in, in, in productivity as a result of things like automation. Um, people don't like to talk about it in the technology community because the sellers especially cause you know, but I think it clearly has to have an impact on jobs. Maybe people don't get fired, but you might hire less people. But that's not really the point I want to make and ask you about. It's the types of jobs that are going to become valuable. We'll shift to these higher value activities. If you're, you know, filling out a form that's going to be less valuable than some of these other more creative, more strategic types of things. What's your point of >>you on that? So, uh, first off, I don't think there's any human that can keep pace with the growth of knowledge work that's getting generated now. So they're going to need help. There's no lack of things to do. So that's kind of my, my, my first thought. I would say my, my second thought in that is, you know, what, if you could use your time differently, I would ask that question to anybody. If you could use your time differently, think about all the value you could go and create. But if you're spending time doing administrivia, is that really the best use of your time? It's clearly not. And so that's where this technology comes into play. The productivity gain is cause you're going to be able to do things that matter the most. Or unleash the creativity of your people. And my experience in working with organizations is exactly that. They leverage automation technology. Now they can do the missions they always wanted to do but never got to in their backlog. >>Yeah. So I guess that my, my take on that, I'd love, I'd love your thoughts. I mean take existing jobs and put a brick wall around them, those existing jobs or are going to change and I think he's going to have a, a negative, you're gonna have job loss, there's no question. And, but then the other jobs are going to be created. My rap on this, people who want to protect the, the past from the future is we basically have 0% unemployment right now. Even in an economic and dramatic economic downturn, we have one 10% unemployment. So if you're, if you're 90% of the people out there, you're going to be able to get a job. Now, nobody likes the economic downturn, but the point is to be competitive as a, as a nation, as a society, you've got to innovate. And automation is part of that innovation. >>Look, I think if you think about the jobs that people want to do, yeah, they're probably not the jobs that are going to be affected by this. And that's what I mean by the evolution. So people can now spend their time on those higher value things. People don't want to do those sets of tasks. Or if you really ask them, think about what they put on the resume, no one puts on their resume today, I'm a great data entry expert. They want to talk about their time with clients, relationship management, making a difference for the business. That's a potential. >>Yeah, but there was a time people would put that on their resume. Punch card, you know, operator. Right. So, right. So we're still, we're still thriving. We're still around. Thanks so much for coming to the queue. It was a great conversation. Thank you. Thanks for hosting me. Pleasure. All right, you're welcome. All right, keep it right there buddy. We'll be back to wrap the IBM data and AI forum from Miami. You're watching the cube. You're right back.

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>We're back in Miami. You're watching the cube, the leader in live tech coverage, and we're covering the IBM data and AI forum in the port of Miami. Mateus Fuka is here, he's the director of offering management for hybrid data management. Everything data. But see, it's great to see you. It's great to have you. So be here with you. We're going to talk database, we're gonna talk data warehouse, everything. Data, you know, did the database market, you know, 10 years ago, 12 years, it was kind of boring. Right. And now it's like data's everywhere. Database is exploding. What's your point of view on what's going on in the marketplace? You know, I mean it's funny too. You used to it boring because I think it's the boring stuff that really matters nowadays to get, get things to where you get people to value with the solutions you want to be or the modernization. >>Thea. Yeah. Seeking to do on the data estates. Um, the challenge that you have in embracing multi-cloud data architectures. So to get, to get to, well you have to, do, I have to take care of the boring stuff. How real is multi-cloud? I mean, I know multi-cloud is, is real and that everybody has multiple clouds. But is multi-cloud a strategy or is it a sort of a symptom of multi-vendor and it just, we could have ended up here with the shadow it and everything else. >> I think it's a reality and yes, it should be a strategy, but I think more more clients and not they find themselves being exposed to this as a reality with different lines of businesses, acquiring data, um, estates running on different locations, different clouds, you know, and then companies have challenge if you want to bring it all together and actually the value of that data, um, and make it available for analytics or AI solutions. >>You know, you've got to have a strategy. >> So IBM is one of the few companies that has both a cloud and an aggressive multi-cloud strategy. Um, you know, Amazon's got outpost a little bit here and Microsoft I guess has some stuff, uh, a but, but generally speaking, uh, Oracle has got a little bit here but IBM has both a cloud. So you'd love people to come into your cloud, but you recognize not everybody's gonna come in your club. So you have an aggressive multi-cloud strategy. Why is that? What's the underpinning of that strategy? Is it openness? Is it just market, you know, total available market? Why? So first of all, yes, we have a, we have a strong portfolio on IBM cloud and we think, you know, it's the best in terms of, you know, integration with other cloud services, the performance you get on the different data services. >>But we also have a strategy that says we want to be our clients want to go. And many clients might have committed already on a strategic level to a different cloud, whether that's AWS, you know, why IBM cloud or Asia. And so we want to be ready as clients want to go. And our commitment is to offer them a complete portfolio of data services that support different workloads. And a complete portfolio in terms of, um, your, the IBM, uh, hope heavy set of technologies as well as open source technologies, give clients choice but then make them available across that universe of multicloud hybrid cloud on premise in a way that they get a consistent experience. And you know, I mean you are familiar with the term. Oh, you divide and conquer, right? I like to talk about it as uh, you know, um, unify to conquer. >>So our, our mission is really unified experience and unified the access to different capabilities available across multicloud architects. So is that really the brand promise gotta unify across clouds? Absolutely. That's our mission. And what's the experience like today and what is sort of the optimal outcome that you guys are looking for? Uh, being able to run any database on any cloud anywhere. Describe that. >> So I think, um, you'd be talking about chapter one and two off the cloud, right? When it, when it comes to chapter one in my, in my view, chapter one was very much about attracting people to the cloud by offering them a set of managed services that take away the management headaches and you know, the, the infrastructure, uh, management aspects. Um, but when you think about chapter two, when you think about how to run, uh, mission critical workloads on, on a cloud or on premise, um, you know, you want to have the ability to integrate data States that run in different environments and we think that OpenShift is leveling the playing field by avoiding location, by, by giving clients the ability to basically abstract from PI, Teri cloud infrastructure services and mechanisms. >>And that gives them freedom of action. They can, they can deploy a certain workload in one in one place and then decide six months later that they are better off moving that workload somewhere else. Yes. >> So OpenShift is the linchpin, absolutely. That cross-cloud integration, is that right? Correct. And with the advent of the rise of the operator, I think you see, you know, you see, um, the industry closing the gap between the value proposition of a fully managed service and what a client managed open shift based environment can deliver in terms of automation, simplicity and annual Oh value. Let's talk about the database market and you're trying to, what's happening? You've got, you know, transactional database, you've got analytic database, you've got legacy data warehouses, you've got new, emerging, emerging, you know, databases that are handling unstructured data. You got to know sequel, not only sequel lay out the landscape and where, what's IBM strategy in the database space? >>So our strategy has, has, so starting with the DB to family, right? We have introduced about two one, two years ago we introduced somebody called Tacoma sequel engine. That gives you a consistent, um, experience in from an application and user perspective in the way you consume, um, data for different workload types. Whether that's transactional data, um, analytical use cases, speak data overdue or fast data solution events, different data architectures, everything, you know, with a consistent experience from a management perspective, from a, from a working behavior perspective in the way you interact with, with this as an application. And, and not only that, but also then make that available on premises in the cloud, fully managed or now open shift based on any cloud. Right. So our, our, I would say our commitment right now is very much focusing on leveraging OpenShift, leveraging cloud pick for data as a platform to deliver all these capabilities DB to an open source in a unified and consistent. >>Uh, I would say with a unified and consistent experience on anybody's cloud, it's like what's in any bag was first, you know, like six months ago when we announced it. And I think now for us doing the same with data and making that data, make it easy for people to access state our way every to the sides is really, but Ts, what's IBM's point of view on, on the degree of integration that you have to have in that stack from hardware and software. So people, some people would argue, well you have to have the same control plane, same data plane, same hardware, same software, same database on prem as you have in the cloud. What's your thoughts on that degree of homogeneity that's required to succeed? So I think it's certainly something that, uh, companies strive to get to simplify the data architectures, unify, consolidate, reduced the number of data sources that you have to deal with. >>But the reality is that the average enterprise client has 168 different data services they have to incorporate, right? So to me it's a moving target and while you want to consolidate, you will never fully get there. So I think our approach is we want to give to client choice best different choice in terms of technologies for for the same workload type. Potentially, whether it's a post test for four transactional workloads for TB, two for transactional workloads, whatever fits the bill, right? And then at the same time, um, at the same time abstract or unify on top of that by, by when you think about operators and OpenShift, for instance, we invest in a, in um, in operators leveraging a consistent framework that basically provides, you know, homogeneous set of interfaces by which people could deploy and life cycle manager Postgres instance or DB two instance. >>So you need only one skillset to manage all these different data services and you know, it reduces total cost of ownership is it provides more agility and, and you know, you know, accelerates time to value for this client. So you're saying that IBM strategy recognizes the heterogeneity within the client base, right? Um, you're not taking, even though you might have a box somewhere in the portfolio, but you're not a, you need this box only strategy. The God box. This is, this is the hammer and every opportunity is a nail. Yeah, we have way beyond that. So we, we are much more open in the way we embrace open source and we bring open source technologies to our enterprise clients and we invest in integration of these different technologies so they can, the value of those can be actuated much more in a much more straightforward fashion. >>The thing about cloud pay for data and the ability to access data insights in different open Sozo, different depositories, IBM, one third party, but then make that data accessible through data virtualization or full governance, applying governance to the data so that data scientists can actually get reef that data for, for his work. That is really important. Can you argue that that's less lock-in than say like they say the God box approach or the cloud only approach? Yeah, absolutely. Because how so? How so? Because, well, because we give you choice to begin with, right? And it's not only choice in terms of the data services and the different technologies that are available, but also in terms of the location where you deploy these data services and how you run them. Okay. So to me it's all about exit strategies. If I go down a path and a path doesn't work for me, how do I get out? >>Exactly. Um, is that a concern of customers in terms of risk management? Yeah. I think, look, every, every costume out there, I daresay, you know, has a data strategy and every customer needs to make some decisions. But you know, there's only so much information you have today to make that decision. But as you learn more, your decision might change six months down the road. And you know, how to preserve that agility as a business to do course corrections I think is really important. So, okay, a hypothetical that this happens every day. You've got a big portfolio companies, they've done a lot of M and a, they've got, you know, 10 different databases that they're running. They got different clouds that they're using, they've got different development teams using, using different tooling, certainly different physical infrastructure. And they really haven't had a strategy to bring it all together. >>Uh, you're hired as the, uh, the data architect or the CTO of the company and say, but Tia's, the CEO says, fix this problem. You're not, we're not taking advantage, uh, and leveraging our data. Where do you start? So of course, being IBM, I would recommend to start with clapping for data as the number one data platform out there because eventually every component will want to capitalize on the value that the data represents. It's not just about a data layer is not just about a database, it's about an indicated solutions tech that gets people to do analytics over the data, the life insights from the data. That's number one. But even if you are, you know, if, if, if it's not I the IBM stack, right, I would always recommend to the client to think about a strategy that that allows for the flexibility change to change course wide and move workloads from one location to another or move data from one technology stack to another. >>And I think that that kind of, you know, that agility and flexibility and, um, translate into, um, risk mitigation strategies that every client should think about. So cloud pack for data, it's okay, let's start there. I'm gonna, I'm gonna, I'm gonna install that, or I'm gonna access that in, into the cloud. And then what do I have to do as a customer to take advantage of that? Do I just have to point it at my data stores? What are the prerequisites? Yeah. So let's say you deploy that on IBM cloud, right? Then you have, you usually are invested already. So you have data, large data estates either residing on share is already in the cloud. You can pull those, those, those datasets in remotely without really moving the workload of the data sets into a cloud pixel, data managed environment by using technologies like data virtualization, right? >>Or using technologies like data stage and ETL capabilities, um, to access the data. But you can also, as you modernize and you build your next next generation application, you can do that within that managed environment with OpenShift. And, and that's what most people want to do. They want to do a digital transformation. They want to modernize the workloads, but we want to leverage the existing investments that they have been making over the last decade. Okay. So, but there's a discovery phase, right, where you bring in cloud pack for data to say, okay, what do I have? Yup, go find it. And then it's bringing in necessary tooling on the, on the diff with the development side with things like OpenShift and then, and then what it's magically virtualizes my data is that, so just on that point, I think you know, the, what made us much more going forward for his clients is how they can incorporate different datasets with adding insure in open source technologies or, or DB two on a third party vendor said, I don't want to mention right now, but, but what matters more is, so how do I make data accessible? >>How do I discover the data set in a way that I can automatically generate metadata? So I have a business glossary, I have metadata and I understand various data sets. Lyft, that's their vision objective business technology objectives. To be able to do that and to what's watching knowledge catalog, which is part of topic for data is a core component that helps you with dead auto discover the metadata generation basically generating, okay, adding data sets in a way that they are now visible to the data scientists and the ultimate end user. What really matters and I think what is our vision overall is the ability to service the ultimate end user medicine developer, a data scientist, so business analysts so that they can get a chip done without depending on it. Yeah, so that metadata catalog is part of the secret sauce that'll that that allows the system to know what data lives, where, how to get to it and and how to join it. >>Since one of the core elements of that, of that integrated platform and solution state board. What I think is really key here is the effort we spend in integrating these different components so that it is, it is, it looks seamlessly, it is happening in an automated fashion that as much as possible and it delivers on that promise of a self service experience for that person that sits at the very end of that. Oh, if that chain right, but to your sex so much for explaining that QA for coming on the cube. Great to meet you. All right. Keep it right there everybody. We'll be back with our next guest right after this short break. You're watching the cube from the IBM data and AI forum in Miami. We'll be right back.

>> Narrator: Live from Miami, Florida, it's The Cube. Covering IBM's Data in AI Forum, brought to you by IBM. >> Welcome back to Miami, everybody. You're watching The Cube, the leader in live tech coverage. We're here covering the IBM Data and AI Forum. Scott Buckles is here to my right. He's the business unit executive at IBM and long time Cube alum, Daniel Hernandez is the Vice President of Data and AI group. Good to see you guys, thanks for coming on. >> Thanks for having us. >> Good to see you. >> You're very welcome. We're going to talk about data ops, kind of accelerating the journey to AI around data ops, but what is data ops and how does it fit into AI? Daniel, we'll start with you. >> There's no AI without data. You've got data science to help you build AI. You've got dev ops to help you build apps. You've got nothing to basically help you prepare data for AI. Data ops is the equivalent of dev ops, but for delivering AI ready data. >> So, how are you, Scott, dealing with this topic with customers, is it resonating? Are they leaning into it, or are they saying, "what?" >> No, it's absolutely resonating. We have a lot of customers that are doing a lot of good things on the data science side. But, trying to get the right data at the right people, and do it fast, is a huge problem. They're finding they're spending too much time prepping data, getting the data into the models, and they're not spending enough time failing fast with some of those models, or getting the models that they need to put in production into production fast enough. So, this absolutely resonates with them because I think it's been confusing for a long time. >> So, AI's scary to a lot of people, right? It's a complicated situation, right? And how do you make it less scary? >> Talk about problems that can be solved with it, basically. You want a better customer experience in your contact center, you want a similarly amazing experience when they're interacting with you on the web. How do you do that? AI is simply a way to get it done, and a way to get it done exceptionally well. So, that's how I like to talk about it. I don't start with here's AI, tell me what problems you can solve. Here are the problems you've got, and where appropriate, here's where AI can help. >> So what are some of your favorite problems that you guys are solving with customers. >> Customer and employee care, which, basically, is any business that does business has customers. Customer and employee care are huge a problem space. Catching bad people, financial crimes investigation is a huge one. Fraud, KYC AML as an example. >> National security, things like that, right? >> Yeah. >> You spend all your time with customers, what else? >> Well, customer experience is probably the one that we're seeing the most. The other is being more efficient. Helping businesses solve those problems quicker, faster. Try to find new avenues for revenue. How to cut costs out of their organization, out of their run time. Those are the ones that we see the most. >> So when you say customer experience, immediately chat bots jumps into my head. But I know we're talking more than, sort of a, transcends chat bots, but double click on customer experience, how are people applying machine intelligence to improve customer experience? >> Well, when I think of it, I think about if you call in to Delta, and you have one bad experience, or your airline, whatever that airline may be, that that customer experience could lead to losing that customer forever, and there used to be an old adage that you have one bad experience and you tell 10 people about it, you have a good one, and you tell one person, or two peoples. So, getting the right data to have that experience is where it becomes a challenge and we've seen instances where customers, or excuse me, organizations are literally trying to find the data on the screen while the customer is on hold. So, they're saying, "can I put you on hold?" and they're trying to go out and find it. So, being able to automate finding that data, getting it in the right hands, to the right people, at the right time, in moment's notice, is a great opportunity for AI and machine learning, and that's an example of how we do it. >> So, from a technical standpoint, Daniel, you guys have this IBM Cloud Pak for Data that's going to magic data virtualization thing. Let's take an example that Scott just gave us, think of an airline. I love my mobile app, I can do everything on my mobile app, except there are certain things I can't do, I have to go to the website. There are certain things I have to do with e-commerce that I have to go to the website that I can't do. Sometimes watching a movie, I can't order a movie from the app, I have to go to website, the URL, and order it there and put it on my watch list. So, I presume that there's some technical debt in each of those platforms, and there's no way to get the data from here, and the data from here talking to each other. Is that the kind of problem that you're solving? >> Yes, and in this particular case, you're actually touching on what we mean by customer and employee care everywhere. The interaction you have on your phone should be the same as the interaction and the kind of response on the web, which should be the same, if not better, when you're talking to a human being. How do you have the exceptional customer and employee care, all channels. Today, say the art is, I've got a specific experience for my phone, a specific experience for my website, a specific, different experience in my contact center. The whole work we're doing around Watson Assistant, and it as a virtual assistant, is to be that nervous system that underpins all channels, and with Cloud Pak for Data, we can deliver it anywhere. You want to run your contact center on an IBM Cloud? Great. You want to run it on Amazon, Azure, Google, your own private center, or everything in between, great. Cloud Pak for Data is how you get Watson Assistant, the rest of Watson and our data stack anywhere you want, so you can deliver that same consistent, amazing experience, all channels, anywhere. >> And I know the tone of my question was somewhat negative, but I'm actually optimistic, and there's a couple examples I'll give. I remember Bill Belichick one time said, "Agh, the weather, it can't ever get the weather right," this is probably five, six years ago. Actually, they do pretty well with the weather compared to 10 or 15 years ago. The other is fraud detection. In the last 10 years, fraud detection has become so much better in terms of just the time it takes to identify a fraud, and the number of false positives. Even in the last, I'd say, 12 to 18 months, false positives are way down. I think that's machine intelligence, right? >> I mean, if you're using business rules, they're not way down. They're still way up. If you're using more sophisticated techniques, that are depending upon the operational data to be trained, then they should be way down. But, there is still a lot of these systems that are based on old school business rules that can't keep up. They're producing alerts that, in many cases, are ignored, and because they're ignored, you're susceptible to bad issues. With, especially AI based techniques for fraud detection, you better have good data to train this stuff, which gets back to the whole data ops thing, and training those with good data, which data ops can help you get done. >> And a key part to data ops is the people and the process. It's not just about automating things and automating the data to get it in the right place. You have to modernize those business processes and have the right skills to be able to do that as well. Otherwise, you're not going to make the progress. You're not going to reap the benefits. >> Well, that was actually my next question. What about the people and the process? We were talking before, off camera, about our PA, and he's saying "pave the cow path." But sometimes you actually have to re-engineer the process and you might not have the skill set. So it's people and process, and then technology you lay in. And we've always talked about this, technology is always going to change. Smart technologists will figure it out. But, the people and the process, that's the hardest part. What are you seeing in the field? >> We see a lot of customers struggling with the people and process side, for a variety of reasons. The technology seems to be the focus, but when we talk to customers, we spend a lot of time saying, "well, what needs to change in your business process "when this happens? "How do those business rules need to change "so you don't get those false positives?" Because it doesn't matter at the end of the day. >> So, can we go back to the business rules thing? So, it sounds like the business rules are sort of an outdated, policy based, rigid sort of structure that's enforced no matter what. Versus machine intelligence, which can interpret situations on the fly, but can you add some color to that and explain the difference between what you call sort of business rules based versus AI based. >> So the AI based ones, in this particular case, probably classic statistical machine learning techniques, to do something like know who I am, right? My name is Danny Hernandez, if you were to Google Danny Hernandez, the number one search result is going to be a rapper. There is a rapper that actually just recently came out, he's not even that good, but he's a new one. A statistical machine learning technique would be able to say, "all right, given Daniel "and the context information I know about him, "when I look for Daniel Hernandez, "and I supplement the identity with that "contextual information, it means it's one of "the six that work at IBM." Right? >> Not the rapper. >> Not the rapper. >> Not the rapper. >> Exactly. I don't mind being matched with a rapper, but match me with a good rapper. >> All you've got to do is search Daniel Hernandez and The Cube and you'll find him. >> Ha, right. Bingo. Actually that's true. So, in any case, the AI based techniques basically allow you to isolate who I am, based on more features that you know about me, so that you get me right. Because if you can't even start there, with whom are you transacting, you're not going to have any hope of detecting fraud. Either that, or you're going to get false positives because you're going to associate me with someone that I'm not, and then it's just going to make me upset, because when you should be transacting with me, you're not because you're saying I'm someone I'm not. >> So, that ties back to what we were saying before, know you're customer and anti money laundering. Which, of course, was big, and still is, during the crypto craze. Maybe crypto is not as crazy, but that was a big deal when you had bitcoin at whatever it was. What are some practical applications for KYC AML that you're seeing in the field today? >> I think that what we see a lot of, what we're applying in my business is automating the discovery of data and learning about the lineage of that data. Where did it come from? This was a problem that was really hard to solve 18 months ago, because it took a lot of man power to do it. And as soon as you did it once, it was outdated. So, we've recently released some capabilities within Watson Knowledge Catalog that really help automate that, so that as the data continues to grow, and continues to change, as it always does, that rather than having two, three hundred business analysts or data stewards trying to go figure that out, machine learning can go do that for you. >> So, all the big banks are glomming on to this? >> Absolutely. >> So think about any customer onboarding, right? You better know who your customer is, and you better have provisions around anti money laundering. Otherwise, there's going to be some very serious downside risk. It's just one example of many, for sure. >> Let's talk about some of the data challenges because we talked a lot about digital, digital business, I've always said the difference between a business and a digital business is how they use data. So, what are some of the challenging issues that customers are facing, and particularly, incumbents, Ginni Rometty used the term a couple of events ago, and it might have even been World of Watson, incumbent disruptors, maybe that was the first think, which I thought was a very poignant term. So, what are some of the data challenges that these incumbents are facing, and how is IMB helping solve them? >> For us, one of them that we see is just understanding where their data is. There is a lot of dark data out there that they haven't discovered yet. And what impact is that having on their analytics, what opportunities aren't they taking advantage of, and what risks are they being exposed to by that being out there. Unstructured data is another big part of it as well. Structured data is sort of the easy answer to solving the data problem, >> [Daniel Hernandez] But still hard. >> But still hard. Unstructured data is something that almost feels like an afterthought a lot of times. But, the opportunities and risks there are equally, if not greater, to your business. >> So yeah, what you're saying it's an afterthought, because a lot of times people are saying, "that's too hard." >> Scott Buckles: Right. >> Forget it. >> Scott Buckles: Right. Right. Absolutely. >> Because there's gold in them there hills, right? >> Scott Buckles: Yeah, absolutely. >> So, how does IBM help solve that problem? Is it tooling, is it discovery tooling? >> Well, yeah, so we recently released a product called InstaScan, that helps you to go discover unstructured data within any cloud environment. So, that was released a couple months ago, that's a huge opportunity that we see where customers can actually go and discover that dark data, discover those risks. And then combine that with some of the capabilities that we do with structured data too, so you have a holistic view of where your data is, and start tying that together. >> If I could add, any company that has any operating history is going to have a pretty complex data environment. Any company that wants to employ AI has a fundamental choice. Either I bring my AI to the data, or I bring my data to the AI. Our competition demand that you bring your data to the AI, which is expensive, hard, often impossible. So, if you have any desire to employ this stuff, you had better take the I'm going to bring my AI to the data approach, or be prepared to deal with a multi-year deployment for this stuff. So, that principle difference in how we think about the problem, means that we can help our customers apply AI to problem sets that they otherwise couldn't because they would have to move. And in many cases, they're just abandoning projects all together because of that. >> So, now we're starting to get into sort of data strategy. So, let's talk about data strategy. So, it starts with, I guess, understanding the value of your data. >> [Daniel Hernandez] Start with understanding what you got. >> Yeah, what data do I have. What's the value of that data? How do I get to that data? You just mentioned you can't have a strategy that says, "okay, move all the data into some God box." >> Good luck. >> Yeah. That won't work. So, do customers have coherent data strategies? Are they formulating? Where are we on that maturity curve? >> Absolutely, I think the advent of the CDO role, as the Chief Data Officer role, has really helped bring the awareness that you have to have that enterprise data strategy. >> So, that's a sign. If there's a CDO in the house. >> There's someone working on enterprise, yeah, absolutely. >> So, it's really their role, the CDO's role, to construct the data strategy. >> Absolutely. And one of the challenges that we see, though, in that, is that because it is a new role, is like going back to Daniel's historical operational stuff, right? There's a lot of things you have to sort out within your data strategy of who owns the data, right? Regardless of where it sits within an enterprise, and how are you applying that strategy to those data assets across the business. And that's not an easy challenge. That goes back to the people process side of it. >> Well, right. I bet you if I asked Jim Cavanaugh what's IBM's data strategy, I bet you he'd have a really coherent answer. But I bet you if I asked Scott Hebner, the CMO of the data and AI group, I bet you I'd get a somewhat different answer. And so, there's multiple data strategies, but I guess it's (mumbles) job to make sure that they are coherent and tie in, right? >> Absolutely. >> Am I getting this? >> Absolutely. >> Quick study. >> So, what's IBM's data strategy? (laughs) >> Data is good. >> Data is good. Bring AI to the data. >> Look, I mean, data and AI, that's the name of the business, that's the name of the portfolio that represents our philosophy. No AI without data, increasingly, not a lot of value of data without AI. We have to help our customers understand this, that's a skill, education, point of view problem, and we have to deliver technology that actually works in the wild, in their environment, not as we want them to be, but as they are. Which is often messy. But I think that's our fun. It's the reason we've been here for a while. >> All right, I'll give you guys a last word, we got to run, but both Scott and Daniel, take aways from the event today, things that you're excited about, things that you learned. Just give us the bumper sticker. >> For me, you talk about whether people recognize the need for a data strategy in their role. For me, it's people being pumped about that, being excited about it, recognizing it, and wanting to solve those problems and leverage the capabilities that are out there. >> We've seen a lot of that today. >> Absolutely. And we're at a great time and place where the capabilities and the technologies with machine learning and AI are applicable and real, that they're solving those problems. So, I think that gets everybody excited, which is cool. >> Bring it home, Daniel. >> Excitement, a ton of experimentation with AI, some real issues that are getting in the way of full-scale deployments, a methodology data ops, to deal with those real hardcore data problems in the enterprise, resonating, a technology stack that allows you to implement that as a company is, through Cloud Pak for Data, no matter where they want to run is what they need, and I'm happy we're able to deliver it to them. >> Great. Great segment, guys. Thanks for coming. >> Awesome. Thank you. >> Data, applying AI to that data, scaling with the cloud, that's the innovation cocktail that we talk about all the time on The Cube. Scaling data your way, this is Dave Vellante and we're in Miami at the AI and Data Forum, brought to you by IBM. We'll be right back right after this short break. (upbeat music)

>>live from Miami, Florida It's the Q covering IBM is data in a I forum brought to you by IBM. >>Welcome back to Miami, Florida Everybody watching the Cube, the leader in live tech coverage. We go out to the events and extract the signal from the noise we're covering the IBM data and a I Forum Scott Hefner series The CMO on uh, sorry VP and CMO IBM Data. Yeah, right, I know. Here's the CMO of late again. So welcome. Welcome to the >>cake was great. Great >>event. Yeah, I've never attended one of these before. The sort of analytics University 1700 people that everybody's like. Sponges trying to learn more and more and more. >>60% higher attendance last year. Awesome. A lot of interest. >>So if we go back a couple of years ago, talks about digital transformation, people roll their eyes. They think it's a buzz word. When you talk to customers, it's really they're trying to transform their business, and data is at the center of that. So if you go back to like 2016 there's a lot of experimentation going on. Kind of throw everything against the wall, see what sticks. It seems Scott, based on the data that I see, that people are now narrowing their their bets on things like Ai ai automation machine learning containers. What are you seeing from customers? >>I think you framed it Well, I mean, if you kind of think about it, this digital transformations been going on for almost 20 years. With the advent of the Internet back around 2000 late 19 nineties, every started on the Internet doing business transactions, and slowly but surely, digital transformation was taken effect, right? And I think clients are now shifting to what we can call digital transformation two point. Oh, what's the next 20 years look like? And our view, our viewpoint from overlay from our clients is, if you think about it, it's data that fuels digital transformation. Right? Without data, there is no digital transformation is no digital. It's all data driven, evidence based decision making, using data to do things more efficiently and more effectively for your clients and your employees, and so on, so forth. But if you think about it, we've been using data as a way of looking to what has happened in the past or what is happening now in clients with digital transformation. To point out what a shift to a word of predictive data. How do you How do you predict in shape? Future outcomes, right? And if you think about it's a I that's gonna unlock predictive data. That's why we see such an intense focus on a I as a really the linchpin of digital transformation. Two point. Oh, and of course, all that data needs to be virtualized. It has to sit in a hybrid cloud environment. 94% of clients have multiple clouds. So if that unlocks the value or if a Iot of Mark's value the data and predictive ways the cloud in a multi cloud environment is that platform that has built upon, it's. That's why you see this enormous shift today. I in terms of investment priority along with hybrid multi cloud. >>So I like this this point of view, this digital transformation 2.0, because what's in their senior business in a digital business? That's how they used data. Yeah, and IBM is mission. Using your group is to help people better take advantage of data to five business outcomes. I mean, that's pretty clearly. What you guys are doing this to Dato To me. Three innovation cocktails, data plus machine intelligence or a I, and then you scale it with cloud. And so you talk about cloud to two point. Oh, really? Involves this predictive sort of a component of the equation that you're bringing into it, doesn't it? >>Yeah. When I think of this next phase, there's several things our clients trying to achieve. One is to predict and shape future outcomes, whether it be inventory, whether it be patient care, whatever it may be. Ah, customer service call. You want Toby to predict what the call's gonna be about what the client or what the customers has gone through before with the issue may be right. So this notion of predicted in shaping the outcome the second is empowering. People do higher value work. How do you make them better at what they're doing? The superpowers of being aided by a machine all right, or some kind of software, it's gonna help you be better what you do. And of course, this whole notion of automating task that people don't want to do automated experiences and intelligent ways. This all adds up to like new business models, right? And that's where a I comes in. That's what I does, and I do think it's a linchpin. What clients are looking to invest in is this notion that you need one unified platform to build upon for the future. That is, cloud service is data service is an aye aye. Service is all is one thing. One cloud native platform that runs on any cloud and completely opens up where all your data is. You run your APS wherever you want to run them secure to the core, and that's what they're looking to invest in. And >>so you guys use is the sort of tag line you can't have a I without. Ay, ay, ay, ay, ay, being information architecture. So for years on the q b been talking about bringing the cloud model to your data? Could you don't move data around? Now you're talking about bringing machine intelligence to your data wherever your data lives, to talk about why that's important and what IBM is doing both conceptually in from a product standpoint, to enable that. >>So the number one issue with the eye and actually a number one issue that sometimes results in failure with a I is didn't understand the data. Some 81% of clients do not understand the data that they're gonna need for the aye aye models. And if they do understand the doubt that they don't know how to make it simple, inaccessible, especially when its ever changing and then they have all the issues of compliance and quality. And is it a trusted set of data that you're using? And that's what you mentioned about? There is no way I without an aye aye, which is information architecture. So it starts there than two. To your point is, Dad is everywhere. There's thousands of sources of data, if not more than that. So how do you normalize all that? Virtual eyes it right. And that's where you get into one platform, any cloud, so that you can access the data wherever it sits. Don't spend the money moving things around the complexity of all that. And then, finally, the third thing we're looking to do is use a I to build. I use a I to actually manage the life cycle of how do you incorporate this into your business and That's what this one platform is gonna d'oh! Versus enabling customers to piece together all this stuff. It's just it's too much. >>So this is what cloudpack for data? Yeah, it is and does. Yes. So you say Aye, aye. Free. Are you talking about picking the functions and automating components? Prioritizing? Yeah. How you apply those those algorithms. Is that right? >>Yeah. So I think Way talk about data with three big things to really focus on his data. And that is the whole nursing. You need that information architecture that's that's ready for an aye aye multicolored world. It's all about the dad in the end, right? Two is about talent, right? Talent being skills. Are you able to acquire the skills you need? So we're trying to help our customers apply. I actually generate and build a I optimize eh? So they don't need is, you know, as much skill to do it. In other words, democratize the ability to build a I models for your business. And then finally, the dad is everywhere. You need to have completely open environment. That's the run on any cloud notion. And that's why the Red had open shift is such a big component of this. So think of clients are looking to climb the ladder >>today. I >>modernize their data states, make the data simple, inaccessible, create a trusted data foundation building scale new models and infuse it throughout their business. Cloudpack for data is essentially the foundational platform that gives you the latter >>day I >>that is in earnestly extensible with things that may be important to you or certain areas of additional capabilities. So Compaq for Dad essentially is the platform that I'm referring to hear when you say you know any cloud, right? >>So I feel like we're on the cusp of this enormous productivity boom. If you look at the data, productivity in the first quarter went up now and if you believe the Bureau of Labor Statistics, but over the long term productivity numbers right, you probably can't believe in them. I think for Q one was like 3% which is a huge uptick. And I feel like it's much, much higher than the anemic whatever it was one and 1/2 1.7%. All this ay, ay, all this automation is gonna drive productivity. It's gonna have an impact on organizations. So what's your perspective? Point of view on on the depending productivity boom boom? Do you believe that premise, How our job's going to be affected, What a client seeing in terms of how their retraining people, What should we expect? >>Yeah, I think a I's gonna give people superpowers. It's gonna make them better. What they do, it's gonna make you as a consumer better at how you choose what to buy. It's gonna make the automobile drive more efficiently and more more information that's relevant to you in the dashboard. It's gonna allow you call for service on your cable company. For them to already know your history, maybe already died. Knows what why you're calling and make it a more efficient call. It's gonna make everyone more productive. It's gonna result in higher quality output because you're able to predict things right. You automate things and intelligent ways, so I don't see it as anything that replaces jobs. It's just gonna make people better at what they do. Allow them to focus on higher value work and be more efficient when you are making decisions right in that will that will result in higher productivity per per worker, right? >>I mean, we've certainly heard examples today of customers that are doing that basically, and it's not like they're firing people. They're basically taking away mundane tasks or things that maybe humans would take so long to do and then re pointing that talent somewhere else. >>Toe higher value. >>So you're seeing that in your client base? Yeah, it's starting to hit today. It's gonna be interesting to see whether or not that affects jobs. I mean, we like to say That's not I ultimately think it's gonna create more jobs. There may be some kind of dip where we've got to retrain people, maybe have to change the way in which we do. Reading right bet Smith and I were talking, reading, writing arithmetic in coding, You know, maybe one of the skills that we have to bring in, but ultimately I think it is a positive, and I'm sanguine and I'm an optimist. Um, but you're seeing examples today of people refocusing their talent. What are they focusing that talent on more strategic things? Like what? >>Well, again, I think it's just getting people to be better at what they do by giving them that predictive power of super powers to be a to do their job better. It's gonna make people better not replace >>them. So it's consumers. We're probably gonna buy more. You're >>gonna buy more, you're gonna buy the right things more. And the right things are gonna be there for you to buy the right sales because everything is gonna be able to better understand patterns of what happens and predict right. And that's why you're seeing this enormous investment shift among among technologists companies. What was that? M. I. T. Sloane in the Boston Consulting Group just came out with a study. I think couple weeks ago, 92% of companies are looking to expand their investments in a I gardener came out with the study of C i ose and there in top investment areas, artificial intelligence was number one. Data and analytics was number two, which is the information architecture, right? One into as the first time it's been like that. So and I think it's for this reason of digital transformation, the predictive notion predictive enterprise, if you will, and just helping everyone be more efficient, more productive or what they do. That's really what it's about. It's not so much replacing people. They're thinking of robots and things like that. That's a small part of what we're talking about. >>Well, even when you talk to people about software robots, they love them because they don't have to do these Monday tests and dramatically impact the quality of what they're doing it again. It frees them up to do other things. >>Good, Good example. Legal Legal Nation is one of our clients that we've been working with, and they do case law for business clients. And sometimes it can take weeks, if not a month, to prepare case law documents. They're able to do that ours now because they have artificial intelligence. The background has done a lot of the case law, intelligence and finding the right dad in the right case law and helping to populate those documents where they don't have to do all the research themselves. So what does that do for the lawyer? Right? It makes them better what they do. They can shift a higher value work than just preparing the document. They could work on more cases that could spend more time on the subtleties of the case. Actually, that's a good example of what we mean here. He's not replacing the lawyer. >>Well, I'm seeing a lot of examples like this in legal fields. Also, auditing. I've talked enough. I've asked you think I'd be able to cut the auditing bill? And the answer is actually, No, because to the point you just made is they're shifting their activities to higher value. They might be charging Maur for activities that take less time. >>Customer service is is another great example. There's so many some examples of that. But it used to be. If you called, everyone treated equal right and you get onto a call. And then sometimes it's very rudimentary things. Sometimes there's gotta be a way to prioritize What are the most critical calls knowing that there's something already wrong and you know why they're calling? And if you can shift your human agents to focus on those and let let a I help with the more rudimentary ones you're making, the client's happier. But those people doing higher value work, we go on forever and ever on just different examples across different industries in different businesses, of how this is really helping people, and it all comes down to it. The three big words, which is prediction, automation and optimization. And that's what I was gonna do. And with digital transformation in just shift the whole the whole notion of using data for evidence based decision making what's happened in the past? What's happening now, too? I'm gonna I'm gonna understand its shape, the future. You could do so many things with that. >>It's amazing when you think about it. We've been at this computer industry 50 60 plus years, and you think everything's automated. It's not even close. All this technology has actually created so much more data so much on structured data. Actually, so many Maur inefficient processes in a lot of ways that now machine intelligence is beginning to attack in a big >>way. You won't find a survey because, ah, a survey of businesses where a eyes not a top aspiration trick, is how do you turn the aspirations of the outcomes? And that's what this latter day eyes all about. It's a very prescriptive approach that we've learned from our clients on howto take that journey to a I and a lot of things we talk about on this on this conversation or the real key linchpins, right? You gotta get the data right. You have to trust in the data that you're going to be used and you gotta get the talent and be able to simple find democratize how you build his models and deploy them. And then ultimately you got to get trust across your organization. And that means the models have to have explained ability, Understand? You have to help you understand how it is recommending these things, and then they're gonna buy into it. It's just gonna make them better. It's the whole notion of superpowers. >>Get that down and then you could scale. And that's really where the business and >>they all want to get there. Now the hard part is now we got to start doing it right. It's kind of like the Internet was 20 years ago. They know they want to do business transactions over the Internet and do commerce. But it didn't happen like overnight. It wasn't magic. It took. It was a journey. I think we're seeing that movie. We playing here? >>Yeah. And in fact, I think in some ways it could even happen faster now because you have the Internet because you have clouds. That's not predicting a very steep Pogue. I've s curve here. We'll have to leave it there. Scott, great to see you. Thanks >>for coming >>on. >>Any time. >>All right. Keep it right, everybody. We'll be back with our next guest right after this short break. You're watching the Cube from the IBM data and a I form in Miami. We'll be right back.

(upbeat music) >> Announcer: Live from Miami, Florida. It's theCUBE. Covering IBM's Data and AI Forum. Brought to you by IBM. >> We're back in Miami everybody. You're watching theCube, the leader in live tech coverage. We go out to the events and extract the signal from the noise we hear. Covering the IBM Data and AI Forum, John Thomas is here, many time CUBE guest. He's not only a distinguished engineer but he's also the chief data scientist for IBM Data and AI. John, great to see you again. >> Great to see you again Dave. >> I'm always excited to talk to you because you're hard core data science. You're working with the customers and you're kind of where the action is. The watchword today is end to end data science life cycle. What's behind that? I mean it's been a lot of experimentation, a lot of tactical things going on. You're talking about end to end life cycle, explain. >> So Dave, what we are saying in our client engagements is, actually working with the data, building the models. That part is relatively easy. The tougher part is to make the business understand what is the true value of this. So it's not a science project, right? It is not a, an academic exercise. So how do you do that? In order for that to happen these models need to go into production. Well, okay, well how do you do that? There is this business of, I've got something in my development environment that needs to move up through QA and staging, and then to production. Well, lot of different things need to happen as you go through that process. How do you do this? See this is not a new paradigm. It is a paradigm that exists in the world of application development. You got to go through a dev ops life cycle. You got to go through continuous integration and continuous delivery mindset. You got to have the same rigor in data science. Then at the front end of this is, what business problem are you actually solving? Do you have business KPIs for that? And when the model is actually is in production, can you track, can you monitor the performance of the model against the business KPIs that the business cares about? And how do you do this on an end to end fashion? And then in there is retraining the model when performance degrades, et cetera, et cetera. But this notion of following dev ops mindset in the world of data science is absolutely essential. >> Dave: So when you think about dev ops, you think of agile. So help me square this circle, when you think end to end data life cycle, you think chewy, big, waterfall, but I'm inferring you're not prescribing a waterfall. >> John: No, no, no. >> So how are organizations dealing with that wholistic end to end view but still doing it in an agile manner? >> Yeah, exactly. So, I always say do not boil the ocean, especially if you're approaching AI use cases. Start with something that is convened, that you can define and break it into springs. So taking an agile approach to this. Two, three springs, if you're not seeing value in those two, three springs, go back to the drawing board and see what is it that you're doing wrong. So for each of your springs, what is the specific successful criteria that you care about and the business cares about? Now, as you go through this process, you need a mechanism to look at, okay, well I've got something in development, how do I move the assets? Not just the model, but, what is the set of features that you're working with? What is the data prep pipeline? What are the scripts being used to evaluate the model? All of these things are logical assets surrounding the model. How do you move them from development to staging? How do you do QA against these set of assets? Then how do you do third party approval oversight? How do you do code review? How do make sure that when you move these assets all of the surrounding mechanisms are being adhered to, compliance requirements, regulatory requirements? And then finally get them to production. So there's a technology aspect of it, obviously. You have a lot of discussion around cube flow, ml flow, et cetera, et cetera as technology options. But there is also mindset that needs to be followed here. >> So once you find a winner, business people want a scale, 'cause they can make more money the more and more times they can replicate that value. And I want to understand this trust and transparent, 'cause when you scale, if you're scaling things that aren't compliant, you're in trouble. But before we get there, I wonder if we can take an example of, pick an industry, or some kind of use case where you've seen this end to end life cycle be successful. >> Yeah, across industries. I mean it's not just specific industry related. But, I'll give you an example. This morning Wunderman Thompson was talking about how they are applying machine learning to, a very difficult problem, which is how to improve how they create a first-time buyer list for their clients. But think of the problem here. It's not just about a one time building of a model. The model needs, okay you got data, understand what data says you're working with, what is the lineage of that data. Once I have their understanding of their data then I get into feature selection, feature engineering, all the steps that I need in your machine learning cycle. Once I am done with selecting my features, doing my feature engineering, I go into model building. Now, it's a pipeline that is being built. It is not a one time activity. Once that model, the pipeline has been vetted, you got to move it from development to your QA environment, from there to your production environment, and so on. And here comes, and this is where it links to the question, transparency discussion. Well the model is in production, how do I make sure the model is being fair? How do I make sure that I can explain what is going on? How do I make sure that the model is not unfairly biased? So all of these are important discussions in the trust and transparency because, you know, people are going to question the outcome of the model. Why did it make a decision? If a campaign was run for an end individual, why did you choose him and not somebody else? If it's a credit card fraud detection scenario, why was somebody tagged as fraudulent and not the other person? If a loan application was rejected, why was he rejected and not someone else? You got to explain this. So, it's not an explain ability that Tom has a lot of, it's over loaded at times, but. The idea here is you should be able to retrace your steps back to an individual scoring activity and explain an individual transaction. You should be able to play back an individual transaction and say version 15 of my model used these features, these hundred features for it's scoring. This was the incoming payload, this was the outcome, and, if I had changed five of my incoming payload variables out of the 500 I use, or hundred I use, the outcome would have been different. Now you can say, you know what, ethnicity, age, education, gender. These parameters did play a role in the decision but they were within the fairness bracket. And the fairness bracket is something that you have to define. >> So, if I could play that back. Take fraud detection. So you might have the machine tell you with 90% confidence or greater that this is fraud but it throws back a false positive. When you dig in, you might see well there's some bias included in there. Then what? You would kind of re-factor the model? >> A couple of different things. Sometimes a bias is in the data itself and it may be valid bias. And you may not want to change that. Well, that's what the system allows you to do. It tells you, this is the kind of bias that exists in the data already. And you can make a business decision as to whether it is good to retain that bias or to correct it in the data itself. Now, if the bias is in how the algorithm is processing their data, again, it's a business decision. Should I correct it or not. Sometimes, bias is not a bad thing. (laughs) It's not a bad thing. No, because, you are actually looking at what signal exists in their data. But what you want to make sure is that it's fair. Now what is fair, that is up to the regulatory body. Are your business defined? You know what, age range between 26 and 45, I want to treat them a certain way. If this is a conscious decision that you, as a business, or your industry is making, that's fair game. But if it is, this is what I wanted that model to do for this age range but the model is behaving a different way, I want to catch that. And I want to either fix the bias in the data or in how the algorithm is behaving with the model itself. >> So, you can eject the edits of the company into the model, but then, and then appropriately and fairly apply that, as long as it doesn't break the law. >> Exactly. (laughs) >> Which is another part of the compliance. >> So, this is not just about compliance. Compliance is a big, big part here. But, this also just answering what your end customer is going to ask. I put in an application for a loan and I was rejected. And, I want an explanation as to why it was rejected, right? >> So you got to be transparent, is your point there. >> Exactly, exactly. And if the business can say, you know what, this is the criteria we used, you fell in this range, and this, in our mind, is a fair range, that is okay. It may not be okay for the end customer but at least you have a valid explanation for why the decision was made by the model. So, it's some black box making some.. >> So the bank might say, well, the decision was made because we don't like the location of the property, we think they're over valued. It had nothing to do with your credit. >> John: Exactly. >> We just don't want to invest in this, by the way, maybe we advise you don't invest in that either. >> Right, right, right. >> So that feedback loop is there. >> This is, being able to find it for each individual transaction, each individual model scoring. What weighed in into the decision that was made by the model. This is important. >> So you got to have atomic access to that data? >> John: At the transaction level. >> And then make it transparent. Are organizations, banks, are they actually making it transparent to their consumers, 'cause I know in situations that I'm involved in, it's either okay go or no but, we're not going to tell you why. >> Everyone is beginning to look into this place. >> Healthcare is another one, right, where we would love more transparency in healthcare. >> Exactly. So this is happening. This is happening where people are looking at oh we can't do just black box in decision making, we have to get serious about this. >> And I wonder, John, if a lot of that black box decision making is just easy to not share information. Healthcare, you're worried about HIPPA. Financial services is just so highly regulated so people are afraid to actually be transparent. >> John: Yup. >> But machine intelligence potentially solves that problem? >> So, internally, at least internal to the company, when the decision is made, you need to have a good idea why the decision was made, right. >> Yeah right. >> As to what you use to explain to the end client or to regulatory body, is up to you. At least internally you need to have clarity on how the decision was arrived at. >> When you were talking about feature selection and feature engineering and model building, how much of that is being done by AI or things like auto AI? >> John: Yup >> You know, versus humans? >> So, it depends. If it's a relatively straightforward use case, you're dealing with 50, maybe a hundred features. Not a big deal. I mean, a good data scientist can sit down and do that. But, again, I'm going back to the Wunderman Thomas example from this morning's keynote, they're dealing with 20,000 features. You just, that is, you just can't do this economically at scale with a bunch of data scientists, even if they're super data scientists doing this in a programmatic way. So this is where something like auto AI comes into play and says, you know what, out of this 20,000 plus feature set, I can select, no. This percentage, maybe a thousand or 2,000 features that are actually relevant. Two, now here comes interesting things. Not just that it has selected 2,000 features out of 20,000, but it says, if I were to take three of these features and two of these features and combine them. Combine them, maybe to do a transpose. Maybe do an inverse of one and multiply it with something else or whatever, right. Do a logarithm make approach to one and then combine it with something else, XOR, whatever, right. Some combination of operations on these features generates a new feature which boosts the signal in your data. Here is the magic, right. So suddenly you've gone from this huge array of features to a small subset and in there you are saying, okay, if I were to combine these features I can now get much better productivity, prediction power for my model. And that is very good, and auto AI is very heavily used in the Wunderman example. In scenarios like that where you have very large scale feature selection, feature engineering. >> You guys use this concept of the data ladder, collect, organize, analyze, and infuse. Correct me if I'm wrong, but a lot of data scientists times is spent collecting, organizing. They want to do more analysis and so ultimately they can infuse. Talk about that analyze portion and how to get there? What kind of progress the industry, generally and IBM is making to help data scientists? >> So analyzers typically.. You don't jump into building machine learning models. The first part is to just do explore re-analysis. You know, age old exploration of your data to understand what is there. I mean people jump into the exhibit first and it's normal, but if you don't understand what your data is telling you, it is foolish to expect magic to happen from your data. So, explorate reanalysis, your traditional approaches. You start there. Then you say, in that context I think I can do model building to solve a particular business problem and then comes the discussion, okay am I using neural nets or am using classical mechanisms, am I doing this framework, XGBoost or Tensorflow? All of that is secondary once you get to explorate reanalysis, looking at framing the business problem as a set of models that can be built, then say what technique do I use now. And auto AI, for example, will help you select the algorithms once you have framed the problem. It's says, should I use lite GBN? Should I use something else? Should I use logistic regression? Whatever, right. So, it is something that the algorithm selection can be helped by auto AI. >> John, we're up against the clock. Great to have you. A wonderful discussion Thanks so much, really appreciate it. >> Absolutely, absolutely. >> Good to see you again. >> Yup, same here. >> All right. Thanks for watching everybody. We'll be right back right after this short break. You're watching theCUBE from the IBM Data and AI Forum in Miami. We'll be right back. (upbeat music)

>> Narrator: Live from Miami, Florida. It's theCUBE. Covering IBM's data and AI forum. Brought to you by IBM. >> Welcome back to the port of Miami everybody. This is theCube, the leader in live tech coverage. We're here covering the IBM AI and data forum. Of course, the centerpiece of IBM's AI platform is Watson. Beth Smith is here, she's the GM of IBM Watson. Beth, good to see you again. >> You too. Always good to be with theCUBE. >> So, awesome. Love it. So give us the update on Watson. You know, it's beyond Jeopardy. >> Yeah, yeah. >> Oh, wow. >> That was a long time ago now. (laughs) >> Right, but that's what a lot of people think of, when they think of Watson. What, how should we think about Watson today? >> So first of all, focus Watson on being ready for business. And then, a lot of people ask me, "So what is it?" And I often describe it as a set of tools, to help you do your own AI and ML. A set of applications that are AI applications. Where we have prebuilt it for you, around a use case. And there is examples where it gets embedded in a different application or system that may have existed already. In all of those cases, Watson is here, tuned to business enterprise, how to help people operational-wise, AI. So they can get the full benefit, because at the end of the day it's about those business outcomes. >> Okay, so the tools are for the super geeks, (Beth laughs) who actually want to go in and build the real AI. >> (laughs) That's right, that's right. >> The APPS are, okay. It's prebuilt, right? Go ahead and apply it. >> That's right. >> And the embedded is, we don't even know we're using it, right? >> That's right, or you may. Like, QRadar with Watson has an example of using Watson inside of it. Or, OpenPages with Watson. So sometimes you know you're using it. Sometimes you don't. >> So, how's the mix? I mean, in terms of the adoption of Watson? Are there enough like, super techies out there, who are absorbing this stuff? Or is it mostly packaged APPS? Is it a mix? >> So it is a mix, but we know that data science skills are limited. I mean, they're coveted, right? And so those are the geeks, as you say, that are using the tool chain as a part of it. And we see that in a lot of customers and a lot of industries around the world. And then from a packaged APP standpoint, the biggest use case of adoption is really around customer care, customer service, customer engagement. That kind of thing. And we see that as well. All around the world, all different industries. Lots of great adoption. Watson Assistant is our flagship in that. >> So, in terms of, if you think about these digital initiatives, we talked about digital transformation, >> Yup. >> Last few years, we kind of started in 2016 in earnest, it's real when you talk to customers. And there was a ton of experimentation going on. It was almost like spaghetti. Throw against the wall and see what sticks. Are you seeing people starting to place their bets on AI, Narrowing their scope, and really driving you know, specific business value now? >> Beth: Yeah. >> Or is it still kind of all over the place? >> Well, there's a lot of studies that says about 51% or so still stuck in experimentation. But I would tell you in most of those cases even, they have a nice pilot that's in production, that's doing a part of the business. So, 'cause people understand while they may be interested in the sexiness of the technology, they really want to be able to get the business outcomes. So yes, I would tell 'ya that things have kind of been guided, focused towards the use cases and patterns that are the most common. You know, and we see that. Like I mentioned, customer care. We see it in, how do you help knowledge workers? So you think of all those business documents, and papers and everything that exists. How do you assist those knowledge workers? Whether or not it's an attorney or an engineer, or a mortgage loan advisor. So you see that kind of use case, and then you see customers that are building their own. Focused in on, you know, how do they optimize or automate, or predict something in a particular line of business? >> So you mentioned Watson Assistant. So tell us more about Watson Assistant, and how has that affected adoption? >> So Watson Assistant as I said, it is our flagship around customer care. And just to give you a little bit of a data point, Watson Assistant now, through our public cloud, SaaS version, converses with 82 million end users a month. So it's great adoption. And this is, this is enabling customers. Customers of our customers, to be able to get self-service help in what they're doing. And Watson Assistant, you know, a lot of people want to talk about it being a chat bot. And you can do simple chat bots with it. But it's to sophisticated assistance as well. 'Cause it shows up to do work. It's there to do a task. It's to help you deal with your bank account, or whatever it is you're trying to do, and whatever company you're interacting with. >> So chat bots is kind of a, (laughs) bit of a pejorative. But you're talking about digital systems, it's like a super chat bot, right? >> Beth: Yeah. I saw a stat the other day that there's going to be, by I don't know, 2025, whatever. There's going to be more money spent on chat bot development, or digital assistance, than there is on mobile development. And I don't know if that's true or not, >> Beth: Mhm, wow. But it's kind of an interesting thing. So what are you seeing there? I mean, again I think chat bots, people think, oh, I got to talk into a bot. But a lot of times you don't know you're, >> Beth: That's right. >> so they're getting, they're getting better. I liken it to fraud detection. You know, 10 years ago fraud detection was like, six months later you'll, >> Right. >> you'll get a call. >> Exactly. >> And so chat bots are just going to get better and better and better, and now there's this super category that maybe we can define here. >> That's right. >> What is that all about? >> That's right. And actually I would tell you, they kind of, they can become the brain behind something that's happening. So just earlier today I was, I was with a customer and talking about their email CRM system, and Watson Assistant is behind that. So chat bots aren't just about what you may see in a little window. They're really about understanding user intent, guiding the user through what they're trying to either find out or do, and taking the action as a part of it. And that's why we talk about it being more than chat bots. 'Cause it's more than a FAQ interchange. >> Yes, okay. So it's software, >> Beth: Yes. >> that actually does, performs tasks. >> Beth: Yes. >> Probably could call other software, >> Beth: Absolutely. >> to actually take action. >> That's right. >> I mean, I see. We think of this as systems of agency, actually. Making, sort of, >> That's right. >> decisions and then I guess, the third piece of that is, having some kind of human interaction, where appropriate, right? >> That's right. >> What do you see in terms of, you know, infusing humans into the equation? >> So, well a couple of things. So one of the things that Watson Assistant will do, is if it realizes that it's not the expert on whatever it is, then it will pass over to an expert. And think of that expert as a human agent. And while it's doing that, so you may be in the queue, because that human person is tied up, you can continue to do other things with it, while you're waiting to actually talk to the person. So that's a way that the human is in the loop. I would tell you there's also examples of how the agents are being assisted in the background. So they have the interaction directly with the user, but Watson Assistant is helping them, be able to get to more information quicker, and narrow in on what the topic is. >> So you guys talk about the AI ladder, >> Beth: Mhm. >> Sort of, Rob talked about that this morning. My first version of the AI ladder was building blocks. It was like data and AI analytics, ML, and then AI on top of that. >> Beth: Yup. >> I said AI. Data and IA. >> Beth: Yup. >> Information Architecture. Now you use verbs. Sort of, to describe it. >> Beth: Yup. Which is actually more powerful. Collect, organize, analyze and infuse. Now infuse is like the Holy Grail, right? 'Cause that's operationalizing and being able to scale AI. >> Beth: That's right. >> What can you tell us about how successful companies are infusing AI, and what is IBM doing to help them? >> So, I'm glad you picked up first of all, that these are verbs and it's about action. And action leads to outcome, which is, I think, critical. And I would also tell you yes, infuse is, you know, the Holy Grail of the whole thing. Because that's about injecting it into business processes, into workflows, into how things are done. So you can then see examples of how attorneys may be able to get through their legal prep process in just a few minutes, versus 10, 15 hours on certain things. You can see conversion rates of, from a sales standpoint, improve significantly. A number of different things. We've also got it as a part of supply chain optimization, understanding a little bit more about both inventory, but also where the goods are along the way. And particularly when you think about a very complicated thing, there could be a lot of different goods in various points of transit. >> You know, I was sort of joking. Not joking, but mentioning Jeopardy at first. 'Cause a lot of people associate Watson with Jeopardy. >> Beth: Right. >> I can't remember the first time I saw that. It had to be the mid part of the last decade. What was it? >> Beth: February of 2011. >> 2011, okay I thought I even saw demos before that. I'm actually sure I did. Like in, back in some lab in IBM. And of course, the potential like, blew your mind. >> Right. >> I suspect you guys didn't even know what you had at the time. You were like, "Okay, we're going to go change the world." And you know, when you drive up and down 101 in Silicone Valley, it's like, "Oh, Watson this, Watson that." You know, you get the consumer guys, doing facial recognition, ad serving. You know, serving up fake news, you know. All kinds of applications. But IBM started to do something different. You're trying to really change business. Did you have any clue as to what you had at the time? And then how much of a challenge you were taking on, and then bring us to where we are now, and what do you see as a potential for the next 10 years? >> So, of course we had a clue. So let me start there. (Dave laughs) But with that, I think the possibilities of it weren't completely understood. There's no question in my mind about that. And what the early days were, were understanding, okay, what is that business application? What's the pattern that's going to come about as a part of it? And I think we made tremendous progress on that along the way. I would tell you now, you mentioned operationalizing stuff, and you know, now it's about, how do we help companies have it more throughout their company? Through different lines of business, how does it tie to various things that are important to us? And so that brings in things like trust, explainablity, the ethics of what it's doing. Bias detection and mitigation. And I actually believe a lot of that, and the operationalizing it within the processes, is where we're going to head, going forward. Of course there'll continue to be advancements on the features and the capabilities, but it's going to be about that. >> Alright, I'm going to ask you the it's depends question. (Beth laughs) So I know that's your answer, but at the macro, can machines make better diagnosis than doctors today, and if not, when will they be able to, in your view? >> So I would actually tell you that today they cannot, but what they can do is help the doctor make a better diagnosis than she would have done by herself. And because it comes back to this point of, you know, how the machine can process so much information, and help the expert, in this case the doctor's the expert, it could be an attorney, it could be an engineer, whatever. Help that expert be able to augment the knowledge that he or she has as a part of it. So, and that's where I think it is. And I think that's where it will be for my lifetime. >> So, there's no question in your mind that machines today, AI today, is helping make better diagnosis, it's just within augmented or attended type of approach. >> Absolutely. >> And I want to talk about Watson Anywhere. >> Beth: Okay, great. >> So we saw some discussion in the key notes and some demos. My understanding is, you could bring Watson Anywhere, to the data. >> That's right. >> You don't have to move the data around. Why is that important? Give us the update on Watson Anywhere. >> So first of all, this is the biggest requirement I had since I joined the Watson team, three and a half years ago. Was please can I have Watson on-prem, can I have Watson in my company data center, etcetera. And you know, we needed to instead, really focus in on what these patterns and use cases were, and we needed some help in the platform. And so thanks to Cloud Pak for data, and the underlying Red Hat OpenShift and container platform, we now are enabled to truly take Watson anywhere. So you can have it on premise, you can have it on the other public clouds, and this is important, because like you said, it's important because of where your data is. But it's also important because the workloads of today and tomorrow are very complex. And what's on cloud today, may be on premise tomorrow, may be in a different cloud. And as that moves around, you also want to protect the investment of what you're doing, as you have Watson customize for what your business needs are. >> Do you think you timed it right? I mean, you kind of did. All this talk about multicloud now. You really didn't hear much about it four or five years ago. For awhile I thought you were trying to juice your cloud business. Saying, "You want, if you want Watson, you got to go to the IBM cloud." Was there some of that, or was it really just, "Hey, now the timing's right." Where clients are demanding it, and hybrid and multicloud and on-prem situations? >> Well look, we know that cloud and AI go hand in hand. So there was a lot of positive with that. But it really was this technology point, because had I taken it anywhere three and a half years ago, what would've happened is, every deployment would've been a unique environment, a unique stack. We needed to get to a point that was a modern day, you know, infrastructure, if you will. And that's what we get now, with a container based platform. >> So you're able to scale it, such that every instance isn't a snowflake, >> That's right. >> that requires customization. >> That's right. So then I can invest in the enhancements to the actual capabilities it is there to do, not supporting multiple platform instantiations, under the covers. >> Well, okay. So you guys are making that transparent to the customer. How much of an engineering challenge is that? Can you share that with us? You got to run on this cloud, on that cloud, or on forever? >> Well, now because of Cloud Pak for data, and then what we have with OpenShift and Kubernetes and containers, it becomes, well, you know, there's still some technical work, my engineering team would tell you it was a lie. But it's simple now, it's straightforward. It's a lot of portability and flexibility. In the past, it would've been every combination of whatever people were trying to do, and we would not have had the benefit of what that now gives you. >> And what's the technical enable there? Is it sort of open API's? Architecture that allows for the interconnectivity? >> So, but inside of Watson? Or the overall platform? >> The overall platform. >> So I would say, it's been, at it's, at it's core it's what containers bring. >> Okay, really. So it's that, it's that. It's the marriage of your tech, >> Yeah. >> with the container wave. >> That's right. That's right. Which is why the timing was critical now, right? So you go back, yes they existed, but it really hadn't matured to a point of broad adoption. And that's where we are now. >> Yeah, the adoption of containers, Kubernetes, you know, micro services. >> Right, exactly. Now it's on a very steep curve. >> Exactly. >> Alright, give your last word on, big take away, from this event. What do you hearing, you know, what are you, some of the things you're most excited about? >> So first of all, that we have all of these clients and partners here, and all the buzz that you see. And that we've gotten. And then the other thing that I would tell you is, the great client examples. And what they're bragging on, because they are getting business outcomes. And they're getting better outcomes than they thought they would achieve. >> IBM knows how to throw an event. (Beth laughs) Beth, thanks so much for coming to theCUBE. >> Thank you, good to >> Appreciate it. >> see you again. >> Alright, great to see you. Keep it right there everybody, we'll be back. This is theCUBE live, from the IBM Data Forum in Miami, we'll be right back. (upbeat instrumental music)

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>Welcome back to downtown Miami. Everybody. We're here at the Intercontinental hotel covering the IBM data AI form hashtag data AI forum. My name is Dave Volante and you're watching the cube, the leader in live tech coverage. Ritika gunner is here. She's the vice president of data and AI expert labs and learning at IBM. Ritika, great to have you on. Again, always a pleasure to be here. Dave. I love interviewing you because you're a woman executive that said a lot of different roles at IBM. Um, you know, you've, we've talked about the AI ladder. You're climbing the IBM ladder and so it's, it's, it's, it's awesome to see and I love this topic. It's a topic that's near and dear to the cubes heart, not only women in tech, but women in AI. So great to have you. Thank you. So what's going on with the women in AI program? We're going to, we're going to cover that, but let me start with women in tech. It's an age old problem that we've talked about depending on, you know, what statistic you look at. 15% 17% of, uh, of, of, of the industry comprises women. We do a lot of events. You can see it. Um, let's start there. >>Well, obviously the diversity is not yet there, right? So we talk about women in technology, um, and we just don't have the representation that we need to be able to have. Now when it comes to like artificial intelligence, I think the statistic is 10 to 15% of the workforce today in AI is female. When you think about things like bias and ethicacy, having the diversity in terms of having male and female representation be equal is absolutely essential so that you're creating fair AI, unbiased AI, you're creating trust and transparency, set of capabilities that really have the diversity in backgrounds. >>Well, you work for a company that is as chairman and CEO, that's, that's a, that's a woman. I mean IBM generally, you know, we could see this stuff on the cube because IBM puts women on a, we get a lot of women customers that, that come on >>and not just because we're female, because we're capable. >>Yeah. Well of course. Right. It's just because you're in roles where you're spokespeople and it's natural for spokespeople to come on a forum like this. But, but I have to ask you, with somebody inside of IBM, a company that I could say the test to relative to most, that's pretty well. Do you feel that way or do you feel like even a company like IBM has a long way to go? >>Oh, um, I personally don't feel that way and I've never felt that to be an issue. And if you look at my peers, um, my um, lead for artificial intelligence, Beth Smith, who, you know, a female, a lot of my peers under Rob Thomas, all female. So I have not felt that way in terms of the leadership team that I have. Um, but there is a gap that exists, not necessarily within IBM, but in the community as a whole. And I think it goes back to you want to, you know, when you think about data science and artificial intelligence, you want to be able to see yourself in the community. And while there's only 10 to 15% of females in AI today, that's why IBM has created programs such as women AI that we started in June because we want strong female leaders to be able to see that there are, is great representation of very technical capable females in artificial intelligence that are doing amazing things to be able to transform their organizations and their business model. >>So tell me more about this program. I understand why you started it started in June. What does it entail and what's the evolution of this? >>So we started it in June and the idea was to be able to get some strong female leaders and multiple different organizations that are using AI to be able to change their companies and their business models and really highlight not just the journey that they took, but the types of transformations that they're doing and their organizations. We're going to have one of those events tonight as well, where we have leaders from Harley Davidson in Miami Dade County coming to really talk about not only what was their journey, but what actually brought them to artificial intelligence and what they're doing. And I think Dave, the reason that's so important is you want to be able to understand that those journeys are absolutely approachable. They're doable by any females that are out there. >>Talk about inherent bias. The humans are biased and if you're developing models that are using AI, there's going to be inherent bias in those models. So talk about how to address that and why is it important for more diversity to be injected into those models? >>Well, I think a great example is if you took the data sets that existed even a decade ago, um, for the past 50 years and you created a model that was to be able to predict whether to give loans to certain candidates or not, all things being equal, what would you find more males get these loans than females? The inherent data that exists has bias in it. Even from the history based on what we've had yet, that's not the way we want to be able to do things today. You want to be able to identify that bias and say all things being equal, it is absolutely important that regardless of whether you are a male or a female, you want to be able to give that loan to that person if they have all the other qualities that are there. And that's why being able to not only detect these things but have the diversity and the kinds of backgrounds of people who are building AI who are deploying this AI is absolutely critical. >>So for the past decade, and certainly in the past few years, there's been a light shined on this topic. I think, you know, we were at the Grace Hopper conference when Satya Nadella stuck his foot in his mouth and it said, Hey, it's bad karma for you know, if you feel like you're underpaid to go complain. And the women in the audience like, dude, no way. And he, he did the right thing. He goes, you know what, you're right. You know, any, any backtrack on that? And that was sort of another inflection point. But you talk about the women in, in AI program. I was at a CDO event one time. It was I and I, an IBM or had started the data divas breakfast and I asked, can I go? They go, yeah, you can be the day to dude. Um, which was, so you're seeing a lot of initiatives like this. My question is, are they having the impact that you would expect and that you want to have? >>I think they absolutely are. Again, I mean, I'll go back to, um, I'll give you a little bit of a story. Um, you know, people want to be able to relate and see that they can see themselves in these females leaders. And so we've seen cases now through our events, like at IBM we have a program called grow, which is really about helping our female lead female. Um, technical leaders really understand that they can grow, they can be nurtured, and they have development programs to help them accelerate where they need to be on their technical programs. We've absolutely seen a huge impact from that from a technology perspective. In terms of more females staying in technology wanting to go in the, in those career paths as another story. I'll, I'll give you kind of another kind of point of view. Um, Dave and that is like when you look at where it starts, it starts a lot earlier. >>So I have a young daughter who a year, year and a half ago when I was doing a lot of stuff with Watson, she would ask me, you know, not only what Watson's doing, but she would say, what does that mean for me mom? Like what's my job going to be? And if you think about the changes in technology and cultural shifts, technology and artificial intelligence is going to impact every job, every industry, every role that there is out there. So much so that I believe her job hasn't been invented yet. And so when you think about what's absolutely critical, not only today's youth, but every person out there needs to have a foundational understanding, not only in the three RS that you and I know from when we grew up have reading, writing and arithmetic, we need to have a foundational understanding of what it means to code. And you know, having people feel confident, having young females feel confident that they can not only do that, that they can be technical, that they can understand how artificial intelligence is really gonna impact society. And the world is absolutely critical. And so these types of programs that shed light on that, that help bridge that confidence is game changing. >>Well, you got kids, I >>got kids, I have daughters, you have daughter. Are they receptive to that? So, um, you know, I think they are, but they need to be able to see themselves. So the first time I sent my daughter to a coding camp, she came back and said, not for me mom. I said, why? Because she's like, all the boys, they're coding in their Minecraft area. Not something I can relate to. You need to be able to relate and see something, develop that passion, and then mix yourself in that diverse background where you can see the diversity of backgrounds. When you don't have that diversity and when you can't really see how to progress yourself, it becomes a blocker. So as she started going to grow star programs, which was something in Austin where young girls coded together, it became something that she's really passionate about and now she's Python programming. So that's just an example of yes, you need to be able to have these types of skills. It needs to start early and you need to have types of programs that help enhance that journey. >>Yeah, and I think you're right. I think that that is having an impact. My girls who code obviously as a some does some amazing work. My daughters aren't into it. I try to send them to coder camp too and they don't do it. But here's my theory on that is that coding is changing and, and especially with artificial intelligence and cognitive, we're a software replacing human skills. Creativity is going to become much, much more important. My daughters are way more creative than my sons. I shouldn't say that, but >>I think you just admitted that >>they, but, but in a way they are. I mean they've got amazing creativity, certainly more than I am. And so I see that as a key component of how coding gets done in the future, taking different perspectives and then actually codifying them. Your, your thoughts on that. >>Well there is an element of understanding like the outcomes that you want to generate and the outcomes really is all about technology. How can you imagine the art of the possible with technology? Because technology alone, we all know not useful enough. So understanding what you do with it, just as important. And this is why a lot of people who are really good in artificial intelligence actually come from backgrounds that are philosophy, sociology, economy. Because if you have the culture of curiosity and the ability to be able to learn, you can take the technology aspects, you can take those other aspects and blend them together. So understanding the problem to be solved and really marrying that with the technological aspects of what AI can do. That's how you get outcomes. >>And so we've, we've obviously talking in detail about women in AI and women in tech, but it's, there's data that shows that diversity drives value in so many different ways. And it's not just women, it's people of color, it's people of different economic backgrounds, >>underrepresented minorities. Absolutely. And I think the biggest thing that you can do in an organization is have teams that have that diverse background, whether it be from where they see the underrepresented, where they come from, because those differences in thought are the things that create new ideas that really innovate, that drive, those business transformations that drive the changes in the way that we do things. And so having that difference of opinion, having healthy ways to bring change and to have conflict, absolutely essential for progress to happen. >>So how did you get into the tech business? What was your background? >>So my background was actually, um, a lot in math and science. And both of my parents were engineers. And I have always had this unwavering, um, need to be able to marry business and the technology side and really figure out how you can create the art of the possible. So for me it was actually the creativity piece of it where you could create something from nothing that really drove me to computer science. >>Okay. So, so you're your math, uh, engineer and you ended up in CS, is that right? >>Science. Yeah. >>Okay. So you were coded. Did you ever work as a programmer? >>Absolutely. My, my first years at IBM were all about coding. Um, and so I've always had a career where I've coded and then I've gone to the field and done field work. I've come back and done development and development management, gone back to the field and kind of seen how that was actually working. So personally for me, being able to create and work with clients to understand how they drive value and having that back and forth has been a really delightful part. And the thing that drives me, >>you know, that's actually not an uncommon path for IBM. Ours, predominantly male IBM, or is in the 50 sixties and seventies and even eighties. Who took that path? They started out programming. Um, I just think, trying to think of some examples. I know Omar para, who was the CIO of Aetna international, he started out coding at IBM. Joe Tucci was a programmer at IBM. He became CEO of EMC. It was a very common path for people and you took the same path. That's kind of interesting. Why do you think, um, so many women who maybe maybe start in computer science and coding don't continue on that path? And what was it that sort of allowed you to break through that barrier? >>No, I'm not sure why most women don't stay with it. But for me, I think, um, you know, I, I think that every organization today is going to have to be technical in nature. I mean, just think about it for a moment. Technology impacts every part of every type of organization and the kinds of transformation that happens. So being more technical as leaders and really understanding the technology that allows the kinds of innovations and business for informations is absolutely essential to be able to see progress in a lot of what we're doing. So I think that even general CXOs that you see today have to be more technically acute to be able to do their jobs really well and marry those business outcomes with what it fundamentally means to have the right technology backbone. >>Do you think a woman in the white house would make a difference for young people? I mean, part of me says, yeah, of course it would. Then I say, okay, well some examples you can think about Margaret Thatcher in the UK, Angela Merkel, and in Germany it's still largely male dominated cultures, but I dunno, what do you think? Maybe maybe that in the United States would be sort of the, >>I'm not a political expert, so I wouldn't claim to answer that, but I do think more women in technology, leadership role, CXO leadership roles is absolutely what we need. So, you know, politics aside more women in leadership roles. Absolutely. >>Well, it's not politics is gender. I mean, I'm independent, Republican, Democrat, conservative, liberal, right? Absolutely. Oh yeah. Well, companies, politics. I mean you certainly see women leaders in a, in Congress and, and the like. Um, okay. Uh, last question. So you've got a program going on here. You have a, you have a panel that you're running. Tell us more about. >>Well this afternoon we'll be continuing that from women leaders in AI and we're going to do a panel with a few of our clients that really have transformed their organizations using data and artificial intelligence and they'll talk about like their backgrounds in history. So what does it actually mean to come from? One of, one of the panelists actually from Miami Dade has always come from a technical background and the other panelists really etched in from a non technical background because she had a passion for data and she had a passion for the technology systems. So we're going to go through, um, how these females actually came through to the journey, where they are right now, what they're actually doing with artificial intelligence in their organizations and what the future holds for them. >>I lied. I said, last question. What is, what is success for you? Cause I, I would love to help you achieve that. That objective isn't, is it some metric? Is it awareness? How do you know it when you see it? >>Well, I think it's a journey. Success is not an endpoint. And so for me, I think the biggest thing I've been able to do at IBM is really help organizations help businesses and people progress what they do with technology. There's nothing more gratifying than like when you can see other organizations and then what they can do, not just with your technology, but what you can bring in terms of expertise to make them successful, what you can do to help shape their culture and really transform. To me, that's probably the most gratifying thing. And as long as I can continue to do that and be able to get more acknowledgement of what it means to have the right diversity ingredients to do that, that success >>well Retika congratulations on your success. I mean, you've been an inspiration to a number of people. I remember when I first saw you, you were working in group and you're up on stage and say, wow, this person really knows her stuff. And then you've had a variety of different roles and I'm sure that success is going to continue. So thanks very much for coming on the cube. You're welcome. All right, keep it right there, buddy. We'll be back with our next guest right after this short break, we're here covering the IBM data in a AI form from Miami right back.

>>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. We're here at the Intercontinental Hotel. You're watching the Cube? The leader and I live tech covered set. Daubert is here. He's the vice president of data and I and a I and the chief data officer of cloud and cognitive software. And I'd be upset too. Good to see you again. >>Good. See, Dave, thanks for having me >>here. The data in a I form hashtag data. I I It's amazing here. 1700 people. Everybody's gonna hands on appetite for learning. Yeah. What do you see out in the marketplace? You know what's new since we last talked. >>Well, so I think if you look at some of the things that are really need in the marketplace, it's really been around filling the skill shortage. And how do you operationalize and and industrialize? You're a I. And so there's been a real need for things ways to get more productivity out of your data. Scientists not necessarily replace them. But how do you get more productivity? And we just released a few months ago, something called Auto A I, which really is, is probably the only tool out there that automates the end end pipeline automates 80% of the work on the Indian pipeline, but isn't a black box. It actually kicks out code. So your data scientists can then take it, optimize it further and understand it, and really feel more comfortable about it. >>He's got a eye for a eyes. That's >>exactly what is a eye for an eye. >>So how's that work? So you're applying machine intelligence Two data to make? Aye. Aye, more productive pick algorithms. Best fit. >>Yeah, So it does. Basically, you feed it your data and it identifies the features that are important. It does feature engineering for you. It does model selection for you. It does hyper parameter tuning and optimization, and it does deployment and also met monitors for bias. >>So what's the date of scientists do? >>Data scientist takes the code out the back end. And really, there's some tweaks that you know, the model, maybe the auto. Aye, aye. Maybe not. Get it perfect, Um, and really customize it for the business and the needs of the business. that the that the auto A I so they not understand >>the data scientist, then can can he or she can apply it in a way that is unique to their business that essentially becomes their I p. It's not like generic. Aye, aye for everybody. It's it's customized by And that's where data science to complain that I have the time to do this. Wrangling data >>exactly. And it was built in a combination from IBM Research since a great assets at IBM Research plus some cattle masters at work here at IBM that really designed and optimize the algorithm selection and things like that. And then at the keynote today, uh, wonderment Thompson was up there talking, and this is probably one of the most impactful use cases of auto. Aye, aye to date. And it was also, you know, my former team, the data science elite team, was engaged, but wonderment Thompson had this problem where they had, like, 17,000 features in their data sets, and what they wanted to do was they wanted to be able to have a custom solution for their customers. And so every time they get a customer that have to have a data scientist that would sit down and figure out what the right features and how the engineer for this customer. It was an intractable problem for them. You know, the person from wonderment Thompson have prevented presented today said he's been trying to solve this problem for eight years. Auto Way I, plus the data science elite team solve the form in two months, and after that two months, it went right into production. So in this case, oughta way. I isn't doing the whole pipeline. It's helping them identify the features and engineering the features that are important and giving them a head start on the model. >>What's the, uh, what's the acquisition bottle for all the way as a It's a license software product. Is it assassin part >>of Cloudpack for data, and it's available on IBM Cloud. So it's on IBM Cloud. You can use it paper use so you get a license as part of watching studio on IBM Cloud. If you invest in Cloudpack for data, it could be a perpetual license or committed term license, which essentially assassin, >>it's essentially a feature at dawn of Cloudpack for data. >>It's part of Cloudpack per day and you're >>saying it can be usage based. So that's key. >>Consumption based hot pack for data is all consumption based, >>so people want to use a eye for competitive advantage. I said by my open that you know, we're not marching to the cadence of Moore's Law in this industry anymore. It's a combination of data and then cloud for scale. So so people want competitive advantage. You've talked about some things that folks are doing to gain that competitive advantage. But the same time we heard from Rob Thomas that only about 4 to 10% penetration for a I. What? What are the key blockers that you see and how you're knocking them >>down? Well, I think there's. There's a number of key blockers, so one is of access to data, right? Cos have tons of data, but being able to even know what data is, they're being able to pull it all together and being able to do it in a way that is compliant with regulation because you got you can't do a I in a vacuum. You have to do it in the context of ever increasing regulation like GDP R and C, C, P A and all these other regulator privacy regulations that are popping up. So so that's that's really too so access to data and regulation can be blockers. The 2nd 1 or the 3rd 1 is really access to appropriate skills, which we talked a little bit about. Andi, how do you retrain, or how do you up skill, the talent you have? And then how do you actually bring in new talent that can execute what you want on then? Sometimes in some cos it's a lack of strategy with appropriate measurement, right? So what is your A II strategy, and how are you gonna measure success? And you and I have talked about this on Cuban on Cube before, where it's gotta measure your success in dollars and cents right cost savings, net new revenue. That's really all your CFO is care about. That's how you have to be able to measure and monitor your success. >>Yes. Oh, it's so that's that Last one is probably were where most organizations start. Let's prioritize the use cases of the give us the best bang for the buck, and then business guys probably get really excited and say Okay, let's go. But to up to truly operationalize that you gotta worry about these other things. You know, the compliance issues and you gotta have the skill sets. Yeah, it's a scale. >>And sometimes that's actually the first thing you said is sometimes a mistake. So focusing on the one that's got the most bang for the buck is not necessarily the best place to start for a couple of reasons. So one is you may not have the right data. It may not be available. It may not be governed properly. Number one, number two the business that you're building it for, may not be ready to consume it right. They may not be either bought in or the processes need to change so much or something like that, that it's not gonna get used. And you can build the best a I in the world. If it doesn't get used, it creates zero value, right? And so you really want to focus on for the first couple of projects? What are the one that we can deliver the best value, not Sarah, the most value, but the best value in the shortest amount of time and ensure that it gets into production because especially when you're starting off, if you don't show adoption, people are gonna lose interest. >>What are you >>seeing in terms of experimentation now in the customer base? You know, when you talk to buyers and you talk about, you know, you look at the I T. Spending service. People are concerned about tariffs. The trade will hurt the 2020 election. They're being a little bit cautious. But in the last two or three years have been a lot of experimentation going on. And a big part of that is a I and machine learning. What are you seeing in terms of that experimentation turning into actually production project that we can learn from and maybe do some new experiments? >>Yeah, and I think it depends on how you're doing the experiments. There's, I think there's kind of academic experimentation where you have data science, Sistine Data science teams that come work on cool stuff that may or may not have business value and may or may not be implemented right. They just kind of latch on. The business isn't really involved. They latch on, they do projects, and that's I think that's actually bad experimentation if you let it that run your program. The good experimentation is when you start identity having a strategy. You identify the use cases you want to go after and you experiment by leveraging, agile to deliver these methodologies. You deliver value in two weeks prints, and you can start delivering value quickly. You know, in the case of wonderment, Thompson again 88 weeks, four sprints. They got value. That was an experiment, right? That was an experiment because it was done. Agile methodologies using good coding practices using good, you know, kind of design up front practices. They were able to take that and put it right into production. If you're doing experimentation, you have to rewrite your code at the end. And it's a waste of time >>T to your earlier point. The moon shots are oftentimes could be too risky. And if you blow it on a moon shot, it could set you back years. So you got to be careful. Pick your spots, picked ones that maybe representative, but our lower maybe, maybe lower risk. Apply agile methodologies, get a quick return, learn, develop those skills, and then then build up to the moon ship >>or you break that moon shot down its consumable pieces. Right, Because the moon shot may take you two years to get to. But maybe there are sub components of that moon shot that you could deliver in 34 months and you start delivering knows, and you work up to the moon shot. >>I always like to ask the dog food in people. And I said, like that. Call it sipping your own champagne. What do you guys done internally? When we first met, it was and I think, a snowy day in Boston, right at the spark. Some it years ago. And you did a big career switch, and it's obviously working out for you, But But what are some of the things? And you were in part, brought in to help IBM internally as well as Interpol Help IBM really become data driven internally? Yeah. How has that gone? What have you learned? And how are you taking that to customers? >>Yeah, so I was hired three years ago now believe it was that long toe lead. Our internal transformation over the last couple of years, I got I don't want to say distracted there were really important business things I need to focus on, like gpr and helping our customers get up and running with with data science, and I build a data science elite team. So as of a couple months ago, I'm back, you know, almost entirely focused on her internal transformation. And, you know, it's really about making sure that we use data and a I to make appropriate decisions on DSO. Now we have. You know, we have an app on her phone that leverages Cognos analytics, where at any point, Ginny Rometty or Rob Thomas or Arvin Krishna can pull up and look in what we call E P M. Which is enterprise performance management and understand where the business is, right? What what do we do in third quarter, which just wrapped up what was what's the pipeline for fourth quarter? And it's at your fingertips. We're working on revamping our planning cycle. So today planning has been done in Excel. We're leveraging Planning Analytics, which is a great planning and scenario planning tool that with the tip of a button, really let a click of a button really let you understand how your business can perform in the future and what things need to do to get it perform. We're also looking across all of cloud and cognitive software, which data and A I sits in and within each business unit and cloud and cognitive software. The sales teams do a great job of cross sell upsell. But there's a huge opportunity of how do we cross sell up sell across the five different businesses that live inside of cloud and cognitive software. So did an aye aye hybrid cloud integration, IBM Cloud cognitive Applications and IBM Security. There's a lot of potential interplay that our customers do across there and providing a I that helps the sales people understand when they can create more value. Excuse me for our customers. >>It's interesting. This is the 10th year of doing the Cube, and when we first started, it was sort of the beginning of the the big data craze, and a lot of people said, Oh, okay, here's the disruption, crossing the chasm. Innovator's dilemma. All that old stuff going away, all the new stuff coming in. But you mentioned Cognos on mobile, and that's this is the thing we learned is that the key ingredients to data strategies. Comprised the existing systems. Yes. Throw those out. Those of the systems of record that were the single version of the truth, if you will, that people trusted you, go back to trust and all this other stuff built up around it. Which kind of created dissidents. Yeah. And so it sounds like one of the initiatives that you you're an IBM I've been working on is really bringing in the new pieces, modernizing sort of the existing so that you've got sort of consistent data sets that people could work. And one of the >>capabilities that really has enabled this transformation in the last six months for us internally and for our clients inside a cloud pack for data, we have this capability called IBM data virtualization, which we have all these independent sources of truth to stomach, you know? And then we have all these other data sources that may or may not be as trusted, but to be able to bring them together literally. With the click of a button, you drop your data sources in the Aye. Aye, within data. Virtualization actually identifies keys across the different things so you can link your data. You look at it, you check it, and it really enables you to do this at scale. And all you need to do is say, pointed out the data. Here's the I. P. Address of where the data lives, and it will bring that in and help you connect it. >>So you mentioned variances in data quality and consumer of the data has to have trust in that data. Can you use machine intelligence and a I to sort of give you a data confidence meter, if you will. Yeah. So there's two things >>that we use for data confidence. I call it dodging this factor, right. Understanding what the dodging this factor is of the data. So we definitely leverage. Aye. Aye. So a I If you have a date, a dictionary and you have metadata, the I can understand eight equality. And it can also look at what your data stewards do, and it can do some of the remediation of the data quality issues. But we all in Watson Knowledge catalog, which again is an in cloudpack for data. We also have the ability to vote up and vote down data. So as much as the team is using data internally. If there's a data set that had a you know, we had a hive data quality score, but it wasn't really valuable. It'll get voted down, and it will help. When you search for data in the system, it will sort it kind of like you do a search on the Internet and it'll it'll down rank that one, depending on how many down votes they got. >>So it's a wisdom of the crowd type of. >>It's a crowd sourcing combined with the I >>as that, in your experience at all, changed the dynamics of politics within organizations. In other words, I'm sure we've all been a lot of meetings where somebody puts foursome data. And if the most senior person in the room doesn't like the data, it doesn't like the implication he or she will attack the data source, and then the meeting's over and it might not necessarily be the best decision for the organization. So So I think it's maybe >>not the up, voting down voting that does that, but it's things like the E PM tool that I said we have here. You know there is a single source of truth for our finance data. It's on everyone's phone. Who needs access to it? Right? When you have a conversation about how the company or the division or the business unit is performing financially, it comes from E. P M. Whether it's in the Cognos app or whether it's in a dashboard, a separate dashboard and Cognos or is being fed into an aye aye, that we're building. This is the source of truth. Similarly, for product data, our individual products before me it comes from here's so the conversation at the senior senior meetings are no longer your data is different from my data. I don't believe it. You've eliminated that conversation. This is the data. This is the only data. Now you can have a conversation about what's really important >>in adult conversation. Okay, Now what are we going to do? It? It's >>not a bickering about my data versus your data. >>So what's next for you on? You know, you're you've been pulled in a lot of different places again. You started at IBM as an internal transformation change agent. You got pulled into a lot of customer situations because yeah, you know, you're doing so. Sales guys want to drag you along and help facilitate activity with clients. What's new? What's what's next for you. >>So really, you know, I've only been refocused on the internal transformation for a couple months now. So really extending IBM struck our cloud and cognitive software a data and a I strategy and starting to quickly implement some of these products, just like project. So, like, just like I just said, you know, we're starting project without even knowing what the prioritized list is. Intuitively, this one's important. The team's going to start working on it, and one of them is an aye aye project, which is around cross sell upsell that I mentioned across the portfolio and the other one we just got done talking about how in the senior leadership meeting for Claude Incognito software, how do we all work from a Cognos dashboard instead of Excel data data that's been exported put into Excel? The challenge with that is not that people don't trust the data. It's that if there's a question you can't drill down. So if there's a question about an Excel document or a power point that's up there, you will get back next meeting in a month or in two weeks, we'll have an e mail conversation about it. If it's presented in a really live dashboard, you can drill down and you can actually answer questions in real time. The value of that is immense, because now you as a leadership team, you can make a decision at that point and decide what direction you're going to do. Based on data, >>I said last time I have one more questions. You're CDO but you're a polymath on. So my question is, what should people look for in a chief data officer? What sort of the characteristics in the attributes, given your >>experience, that's kind of a loaded question, because there is. There is no good job, single job description for a chief date officer. I think there's a good solid set of skill sets, the fine for a cheap date officer and actually, as part of the chief data officer summits that you you know, you guys attend. We had were having sessions with the chief date officers, kind of defining a curriculum for cheap date officers with our clients so that we can help build the chief. That officer in the future. But if you look a quality so cheap, date officer is also a chief disruption officer. So it needs to be someone who is really good at and really good at driving change and really good at disrupting processes and getting people excited about it changes hard. People don't like change. How do you do? You need someone who can get people excited about change. So that's one thing. On depending on what industry you're in, it's got to be. It could be if you're in financial or heavy regulated industry, you want someone that understands governance. And that's kind of what Gardner and other analysts call a defensive CDO very governance Focus. And then you also have some CDOs, which I I fit into this bucket, which is, um, or offensive CDO, which is how do you create value from data? How do you caught save money? How do you create net new revenue? How do you create new business models, leveraging data and a I? And now there's kind of 1/3 type of CDO emerging, which is CDO not as a cost center but a studio as a p N l. How do you generate revenue for the business directly from your CDO office. >>I like that framework, right? >>I can't take credit for it. That's Gartner. >>Its governance, they call it. We say he called defensive and offensive. And then first time I met Interpol. He said, Look, you start with how does data affect the monetization of my organization? And that means making money or saving money. Seth, thanks so much for coming on. The Cube is great to see you >>again. Thanks for having me >>again. All right, Keep it right to everybody. We'll be back at the IBM data in a I form from Miami. You're watching the Cube?

>>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.

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>Welcome everybody to the port of Miami. My name is Dave Vellante and you're watching the cube, the leader in live tech coverage. We go out to the events, we extract the signal from the noise and we're here at the IBM data and AI form. The hashtag is data AI forum. This is IBM's. It's formerly known as the, uh, IBM analytics university. It's a combination of learning peer network and really the focus is on AI and data. And there are about 1700 people here up from, Oh, about half of that last year, uh, when it was the IBM, uh, analytics university, about 600 customers, a few hundred partners. There's press here, there's, there's analysts, and of course the cube is covering this event. We'll be here for one day, 128 hands-on sessions or ER or sessions, 35 hands on labs. As I say, a lot of learning, a lot of technical discussions, a lot of best practices. >>What's happening here. For decades, our industry has marched to the cadence of Moore's law. The idea that you could double the processor performance every 18 months, doubling the number of transistors, you know, within, uh, the footprint that's no longer what's driving innovation in the it and technology industry today. It's a combination of data with machine intelligence applied to that data and cloud. So data we've been collecting data, we've always talked about all this data that we've collected and over the past 10 years with the advent of lower costs, warehousing technologies in file stores like Hadoop, um, with activity going on at the edge with new databases and lower cost data stores that can handle unstructured data as well as structured data. We've amassed this huge amount of, of data that's growing at a, at a nonlinear rate. It's, you know, this, the curve is steepening is exponential. >>So there's all this data and then applying machine intelligence or artificial intelligence with machine learning to that data is the sort of blending of a new cocktail. And then the third piece of that third leg of that stool is the cloud. Why is the cloud important? Well, it's important for several reasons. One is that's where a lot of the data lives too. It's where agility lives. So cloud, cloud, native of dev ops, and being able to spin up infrastructure as code really started in the cloud and it's sort of seeping to to on prem, slowly and hybrid and multi-cloud, ACC architectures. But cloud gives you not only that data access, not only the agility, but also scale, global scale. So you can test things out very cheaply. You can experiment very cheaply with cloud and data and AI. And then once your POC is set and you know it's going to give you business value and the business outcomes you want, you can then scale it globally. >>And that's really what what cloud brings. So this forum here today where the big keynotes, uh, Rob Thomas kicked it off. He uh, uh, actually take that back. A gentleman named Ray Zahab, he's an adventure and ultra marathon or kicked it off. This Jude one time ran 4,500 miles in 111 days with two ultra marathon or colleagues. Um, they had no days off. They traveled through six countries, they traversed Africa, the continent, and he took two showers in a 111 days. And his whole mission is really talking about the power of human beings, uh, and, and the will of humans to really rise above any challenge would with no limits. So that was the sort of theme that, that was set for. This, the, the tone that was set for this conference that Rob Thomas came in and invoked the metaphor of superheroes and superpowers of course, AI and data being two of those three superpowers that I talked about in addition to cloud. >>So Rob talked about, uh, eliminating the good to find the great, he talked about some of the experiences with Disney's ward. Uh, ward Kimball and Stanley, uh, ward Kimball went to, uh, uh, Walt Disney with this amazing animation. And Walter said, I love it. It was so funny. It was so beautiful, was so amazing. Your work 283 days on this. I'm cutting it out. So Rob talked about cutting out the good to find, uh, the great, um, also talking about AI is penetrated only about four to 10% within organizations. Why is that? Why is it so low? He said there are three things that are blockers. They're there. One is data and he specifically is referring to data quality. The second is trust and the third is skillsets. So he then talked about, you know, of course dovetailed a bunch of IBM products and capabilities, uh, into, you know, those, those blockers, those challenges. >>He talked about two in particular, IBM cloud pack for data, which is this way to sort of virtualize data across different clouds and on prem and hybrid and and basically being able to pull different data stores in, virtualize it, combine join data and be able to act on it and apply a machine learning and AI to it. And then auto AI a way to basically machine intelligence for artificial intelligence. In other words, AI for AI. What's an example? How do I choose the right algorithm and that's the best fit for the use case that I'm using. Let machines do that. They've got experience and they can have models that are trained to actually get the best fit. So we talked about that, talked about a customer, a panel, a Miami Dade County, a Wunderman Thompson, and the standard bank of South Africa. These are incumbents that are using a machine intelligence and AI to actually try to super supercharge their business. We heard a use case with the Royal bank of Scotland, uh, basically applying AI and driving their net promoter score. So we'll talk some more about that. Um, and we're going to be here all day today, uh, interviewing executives, uh, from, uh, from IBM, talking about, you know, what customers are doing with a, uh, getting the feedback from the analysts. So this is what we do. Keep it right there, buddy. We're in Miami all day long. This is Dave Olanta. You're watching the cube. We'll be right back right after this short break..