 Live from San Francisco, California, it's theCUBE. Covering the IBM Chief Data Officer Summit. Brought to you by IBM. We're back at Fisherman's Wharf, covering the IBM Chief Data Officer event. The 10th anniversary, you're watching theCUBE, the leader in live tech coverage. Just off the keynotes, Martin Schroeder is here, as the Senior Vice President of IBM Global Markets, responsible for revenue, profit, IBM's brand, just a few important things. Martin, welcome to theCUBE. They're important. Inderpal Bhandari, Cube alum, Global Chief Data Officer at IBM, good to see you again. Good to see you, Dave. So you guys, just off the keynotes, Martin, you talked a lot about disruption, things like digital trade that we're going to get into, digital transformation. What are you hearing when you talk to clients? You spent a lot of time as the CFO. I did. Now you spend a lot of time with clients. What are they telling you about disruption and digital transformation? Yeah, you know, the interesting thing, Dave, the first thing that every CEO starts with now is that I run a technology company, and it doesn't matter if they're writing code or if they're manufacturing corrugated cardboard boxes. Every CEO believes they are running a technology company. Now interestingly, maybe we could have predicted this already five or six years ago because we run a CEO survey, we run CFO, we run surveys of the C-suite, and already about five years ago, technology was number one on the CEO's list of what's going to change their company in the next three to five years. It led, the CFO lagged, the CMO lagged, everyone else lagged. CEOs saw it first. So CEOs now believe they are running technology businesses. And then when you run a technology business, that means you have to fundamentally change the way you work, how you work, who does the work, and how you're finding and reaching and engaging with your clients. So when we talk, you know, we shorthand of digitizing the enterprise, or what does it mean to become a digitally enabled enterprise? It really is about how do you use today's technology embedded into your workflows to make sure you don't get disintermediated from your clients and you're bringing them value at every step, every touch point of their journey. So that brings up a point. Every CEO I talked to was trying to get digital right. And that comes back to the data. Now you're of course biased on that. But what are your thoughts on a digital business? Digital business is all about how they use data and leverage data. What does it mean to get digital right in your view? So data has to be the starting point. You actually do see examples of companies that will start out on a digital transformation or a technology transformation, and then eventually back into the data transformation. So in a sense, you've got to have the digital piece of it which is really the experience that users have of the products of the company, as well as the technology which is kind of the back end engines that are running, but also the workflow and being able to infuse AI into workflows and then data because everything really rides on the data being in good enough shape to be able to pull all this off. So eventually people realize that really it's not just a digital transformation or a technology transformation, but it is a data transformation to begin with. And you guys have talked a lot at this event, at least this pre-event. I've talked to people about operationalizing AI. That's a big part of your responsibilities. How do you feel about where you're at? I mean, it's a journey I know you've never done, but I feel like you're making some good progress there. Injournally at IBM specifically. Internally at IBM, very good progress because our whole goal is to infuse AI into every major business process and touch every IBM. So that's the whole goal of what we've been doing for the last few years. And we're already at the stage where our central AI and data platform for this year, over 100,000 active users will be making use of it on a regular basis. So we think we're in pretty far along in terms of our transformation and the whole goal behind this summit and the previous summits, as you know Dave, has been to use that as a showcase for our clients and customers so that they can replicate that journey as well. So we heard Ginni Rametti, two IBM things to go, talk about incumbent disruptors, which resonates as IBM's an incumbent disruptor. You talked about chapter one being random acts of digital. And then chapter two is sort of how to take that mainstream. So what do you see as the next wave, Martin? Well, as Inderpal said, and if I use us as an example, now we are using AI heavily. We have an advantage, right? We have this thing called IBM Research, one of the most prolific inventors of things, still leads the world, we still lead the world in patents. So we have the benefit. For our clients, however, we have to help them down that journey. And the clients today are on a journey of finding the right hybrid cloud solution. That gives them bridges, sort of, I have this data, the incumbency advantage of having data, along with where are the tools and where is the compute power that I need to take advantage of the data? So they're on that journey. At the same time, they're on the journey as Inderpal said, of embedding it into their workflows. So for IBM, the company that's always lived sort of at the intersection of technology and business, that's what we're helping our clients do today, helping them take their incumbent advantage of data, having data, helping them co-create or working with them to co-create solutions that they can deploy, and then helping them put that into work, into production, if you will, in their environments and in their workflows. So one of the things you stress today, two of the things, you talk about transparency and open digital trade. I want to get into the latter, but think, talk about what's important in chapter two, just one of those ingredients of success. You talked about things like free flow of data, prevent data localization, mandates, and protect algorithms and source codes. You also made another statement, which is very powerful. IBM has never given up its source code to a government, and we'd leave the country first. So what are some of those success factors that we need to be thinking about in that context? So let's, if we look at IBM, IBM today runs 87% of the world's credit card transactions, right? IBM today runs the world's banking systems, we run the airline reservation systems, we run the supply chains of the world, hearts and lungs, right? If I just shorthand all of that, hearts and lungs, the reason our clients allow us to do that is because they trust us at the very core. If they didn't trust us with their data, they wouldn't give it to us. If they didn't trust us to run the process correctly, they wouldn't give it to us. So when we say trust, it happens at a very base level of who do you really trust to run your data, and importantly, who is someone else going to trust with your data, with your systems? Any bank can maybe figure out how to run a little bit of a process, but you need scale, that's where we come in, so big banks need us, and secondly, you need someone you can trust that can get into the global banking system because the system has to trust you as well. So they trust us at a very base level, that's why we still, as I said, we still run the hearts and lungs of the enterprise world. And you also made the point, you're not talking about necessarily personal data, that's not your business, but when you talked about the free flow of data, there are governments of many, Western governments who are sort of putting in this mandate of not being able to persist data out of the country, but then you gave an example of, if you're trying to track a bag, you know, a baggage claim, you actually want that free flow of data, so what are those conversations like? So first, I do think we have to distinguish between the kinds of data that should flow freely and the kinds of data that should absolutely be, personal information is not what we're talking about. But the supply chains of the world work on data, the banking system works on data, right? So when we talk about the data that has to flow freely, it's all the data that doesn't have a good reason for it to stay local. Citizens' data, healthcare data might have to stay because they're protecting their citizens' privacy. That's the issue I think that most governments are on, so we have to desegregate the data discussion, the free flow of data from the privacy issues, which are very important. Is there a gray area there between the personal information and the type of data that Martin's talking about, or is it pretty clear cut in your view? No, I think this has obviously got to play itself out, but I'll give you one example. So the whole use of a blockchain potentially helps you address and find the right balance between privacy of sensitive data versus actually the free flow of data. So for instance, you could have an encryption or a hash of say the person's name whose luggage is lost, and you could pass that information through, and then on the other side it's decrypted, and then you're able to make sure that essentially you're able to satisfy the client, the customer. So there's flow of data, there's no issue with regard to exposure, because only the rightful parties are able to use it. So these things are, in a sense, the technologies that we're talking about that Martin talked about, with the blockchain and so forth, they are in place to be able to really revolutionize and transform digital trade. But there are other factors as well, with Martin touched on a bunch of those in the keynote with regard to the imbalances, some of the protectionism that comes in and so on and so forth, which all that stuff has to be played through. So much to talk about, so little time. So digital trade, let's get into that a little bit. What is that and why is it so important? So if you look at the economic throughput in the digital economy, the size of the GDP, if you will, of what travels around the world and the way data flows, it's greater than the traded goods flow. So this is a very important discussion. Over the last 10 years, you know, out of the 100% of jobs that were created, 80% or so had a digital component to it, which means that the next set of jobs that we're creating, they require digital skills, right? So we need a set of skills that will enable a workforce, and we need a regulatory environment that's cooperative, that's supportive. So in the regulatory environment, as we said before, we think data should flow freely unless there's a reason for it not to flow. And I think there will be some really good reasons why certain data should not flow, but data should flow freely except for certain reasons that are important. We need to make sure that we don't create a series of mandates that force someone to store data here, right? If you want to be in business in a country, the country shouldn't say, well, if you want to do business here, you have to store your data here. It tends to be done on the auspice of a security concern, but we know enough about security to know that doesn't help, it's a false sense of security. So data has to flow freely, don't make someone store it there just because it may be moving through or is being processed in your country. And then thirdly, we have to protect the source code that companies are using. We cannot force, no country should force a company to give up their source code. People will leave, they just won't do business there. That's just not about an intellectual property issue there, right? It's a huge intellectual property issue, that's exactly right. So the public policy framework then is really free flow of data where it makes sense. No mandates unless it makes sense, right? And protection of IP. Protection of IP, that's right. Okay, good. It's a pretty simple structure and based on my discussions, I think most sort of align with that. And we're encouraged, I'm encouraged by what I see in TPP, it has that. What I see in Europe, it has that. What I see in USMCA, it has that. So all three of those are very good, but they're three separate things. We need to bring it all together to have one. So those are good examples. GDPR maybe is a framework that seems to be seeping its way into other areas. Yeah, so GDPR is an important discussion, but that's the privacy discussion wrapped around a broader trade issue, right? But privacy is important. GDPR does a good job on it, but we have a broader trade issue of data. All right, Indipal, we'll give you the final word. It's kind of your show. Oh, no, so I was just going to say, Dave, I think one way to think about it is you have to have the free flow of data. Maybe the way to think about it is, certain data you do need controls on and it's more the form in which the data flows that you restrict as opposed to letting the data flow at all. What do you mean? The hash example that I gave you. It's okay for the hash to go across, but that way you're not exposing the data itself. So those technologies are all there. It's much more the regulatory frameworks that Martin's talking about, that they've got to be there and in place so that we are not impeding the progress. That's going to be inevitable when you do have the free flow. So in that instance, the hash example that you gave, it's the parties that are adjudicating, the machines are adjudicating. Unless the parties want to expose that data, it won't be exposed. It won't happen if they won't be exposed. All right, Inder Paul, Martin, I know you've got to run. Thanks so much for coming to theCUBE. Appreciate it, you're welcome. All right, keep it right there, everybody, we'll be back with our next guest from IBM CDO Summit in San Francisco. You're watching theCUBE.