 from San Francisco, California. It's theCUBE, covering the IBM Chief Data Officer Summit. Brought to you by IBM. Welcome back to Fisherman's Wharf in San Francisco, everybody, my name is Dave Vellante, and you're watching theCUBE, the leader in live tech coverage. We go out to events, we extract the signal from the noise, we're here at the IBM CDO event. This is the 10th anniversary of this event. Caitlin Halford is here, she's the director of AI Accelerator and Client Success at IBM. Caitlin, great to see you again. Wow, 10 years, amazing. Thank you, thank you. And Carlo Appaglaci is here, who is the program director for data and AI at IBM. Good to see you again, my friend. Thanks for coming on, two CUBE alums. Wow, this is 10 years. And I think theCUBE has covered probably eight of these now. Kind of we bounced between San Francisco and Boston, two great places for CDOs, good places to have intimate events, but, and you're taking it global, I understand. Congratulations, congratulations on a promotion. Thank you. It's all going. Thank you so much. So we, as you know, well know, we started our Chief Data Officer Summit in San Francisco here in San Francisco in 2014. So this is our 10th one, we do two a year. We found we really have a unique cohort of clients that join us, about 140 in San Francisco in the spring, 140 in Boston in the fall, and we're here celebrating the 10th summit. So Carlo, talk about your role and then let's get into how you guys work together, how you hand the baton off, and then we'll get to the client piece. Yeah, so I lead the data center lead team, which is a group within our product development working side by side with clients really to understand their needs, as well as develop use cases on our platform and tools, and make sure we are able to deliver on those. And then we work closely with the CDO team, the global CDO team on best practices, what patterns they're seeing from an architecture perspective, and make sure that our platforms really incorporate in that stuff. And if I recall, the data science lead team is pre-sales, correct? Yep, well it could be post-sales. It could be post-sales, okay. It really depends on the client. So it could be prior to them buying software, or after they bought the software, if they need the help, we can also come in. Okay, so it can be a four pay service, is that correct, or? Yeah, it can be four pay, or sometimes we do it based on just our relationship with the client. And it's kind of a mix then, right? And okay, so you're learning, the client's learning, they're obviously good customers, so you want to treat them right. Now how do you guys work together? Maybe Caitlin, you can explain the two organizations. We're often the early testers, early adopters of some of the capabilities. And so what we'll do is we'll test, we'll iterate, we'll prove it out at scale internally, using IBM itself as an example. And then as we build out the capability, work with Carlo and his team to really drive that into product and drive that into market. And we share a lot of client relationships where CDOs come to us, they want advice and counsel on best practices across the organization, they're looking for latest AI applications to deploy in their own environments. And so we can capture a lot of that feedback and some of the market user testing, prove that out using IBM as an example, and then work with you to really commercialize and bring it to market in the most efficient manner. So you were talking this morning, you had a picture up of the first CDO event, no internet, no Wi-Fi in the basement, love it. So how has this evolved from a theme standpoint? What are the patterns that we should be looking at? So when we started this, it was really in response to primarily financial services sector regulatory requirements, trying to get data right to meet those regulatory and compliance initiatives, defensive posture, certainly weren't driving transformation within their enterprises. And what I've seen is, couple of those core elements are still key for us, so data governance and data management and some of those security access controls, those are always going to be important. But we're finding as CDOs more and more have expanded scope of responsibilities within their enterprise. They're looked at as a leader, they're no longer sitting within a CIO function, they're either a peer or working in partnership with, and they're driving enterprise-wide initiatives for their enterprises and organizations, which has been great to see. So we all remember when Hal Varian declared data science was going to be the number one job and it actually kind of has become. I think I saw somewhere, maybe it was in Glassdoor, it was anointed that the top job, which is kind of cool to see. So what are you seeing with customers, Carlo? You guys, you have these blueprints, you're now applying them, accelerating different industries, you mentioned healthcare this morning. What are some of those industry accelerators and how is that actually coming to fruition? Yeah, so some of the things we're seeing is, speaking of financial clients, we go into a lot of them, we do these one-on-one engagements, we build them from custom, we co-create these engineering solutions on our platform, and we're seeing patterns, patterns around different use cases that are coming up over and over again. And the one thing about data science and AI, it's difficult to develop a solution because everybody's data is different, everybody's business is different. So what we're starting to do is build these, we can't just build a widget, that's going to solve a problem, because then you have to force your data into that and we're seeing that that doesn't really work. So building a platform for these clients with these accelerators, which are a set of core code, source code, notebooks, industry models and terms, as well as dashboards, that allow them to quickly build out these use cases around churn or segmentation and some other models, we can right out of the box provide the models, provide the know-how with the source code, as well as a way for them to train them, deploy them and operationalize them in their organization. That's kind of what we're doing. You prime in the pump. Prime in the pump, we call them, right now we're doing client aids for our wealth management and we're doing that for FSS and they come right out of the box of our CloudPak for data platform. You can quickly click an install button and in there you'll get the sample data files, you get notebooks, you get industry terms, your governance capability as well as deployed dashboards and models. So talk more about CloudPak for data, what's inside of that? CloudPak for data is a collection of microservices and it includes a lot of things that we bring to market to help customers with their AI journey. Things from data ingestion collection to all the way to AI model development from building your models to deploying them to actually infusing them in your business process with bias detection or integration. We have a lot of capability part of the platform. So it's actually tooling. It's not just sort of how-to PDFs, is that right? No, it's an entire platform. So the platform itself has everything you need in an organization to kind of go from an idea to data ingestion and governance and management all the way to model training and development deployment and to integration into your business process. Now, Caitlin, in the early days of the CDO, you saw a CDO emerging in healthcare, financial services and government. And then now it's kind of gone mainstream to the point where we had Mark Claron who's the head of data enablement at AstraZeneca and he said, I'm not taking the CDO title. Because I'm all about data enablement and the CDO title has sort of evolved. But what have you seen? It's clearly gone mainstream. What are you seeing in terms of adoption of that role and its impact on organizations? So a couple of trends has been interesting both domestically and internationally as well. So we're seeing a lot of growth outside of the US. So we did our first inaugural summit in Tokyo in Japan. There's a number of data leaders in Japan that are really eager to jumpstart their transformation initiatives. Also did our first Dubai summit in Middle East and Africa. I'll be in South Africa next month at another CDO summit. And what I'm seeing is outside of North America a lot of activity and interest in creating and enabling a CDO-like capability data leader-like. And some of these guys I think are going to leapfrog ahead. I think they're going to just absolutely jump ahead and in parallel those traditional industries, there's new federal legislation coming down by year-end for most federal agencies to appoint a chief data officer. So Washington DC is hopping right now. We're getting a number of agencies requesting advice and counsel on how to set up the office, how to be successful. I think there's some great opportunity in those traditional industries and also seeing it outside the US and cross nontraditional. So when you say jump ahead, you mean jump ahead of where maybe some of the US best practice. Absolutely, absolutely. And I'm seeing a trend where a lot of CDOs are moving. They're really closer to the line of business, right? They're moving outside of technology, but they have to be technology savvy. They have a team of engineers and data scientists. So it's really an important role in every organization. And I'm seeing it for every client I go to, it's a little different, but you're right. It's definitely an up-and-coming role and it's very important for, especially for digital transformation. This is the, go ahead. I was going to say one of the ways, you know, our teams really partner well together, I think is we can source some of these in terms of enabling that acceleration and leapfrog. What are those pain points or use cases in the traditional data management space, the metadata. So I think you talked with Steven earlier about how we're doing some automated metadata generation and really using AI to, instead of manually having to label and tag that, we're able to generate about 85% of our labels internally and drive that into existing product that Carlo's using and our clients are saying, hey, we're spending hundreds of millions of dollars and we've got teams of massive teams of people, manual work, and so we're able to recognize it, adopt something like that for us internally and then work with you guys to bring it to market. Yeah, absolutely. I think of every ETL developer out there that has to go figure out what this data is. If you have a tool, which we're trying to incorporate in the platform, based on the guidance from the global CDO team, we can automatically create that metadata, automatically ingest it and provide it into a platform so that data scientists can start to get value out of it quickly. So we heard Martin Schroeder talk about sort of digital trade and public policy and he said there were three things, free flow of data, unless it doesn't make sense, like personal information, prevent data localization mandates and then protect algorithms and source code, which is an IP protection thing. So I'm interested in how your customers are reacting to that framework. I presume the protect algorithms and source code, IP, that's near and dear. They want to make sure that you're not taking models and then giving it to their competitors. Absolutely, and we talk about that every time we go in there and we work on projects. What's the IP? You know, how do we manage this? And what we bring to the table with the accelerators is to help them jumpstart them. Even though it's kind of our IP, we create it, but we give it to them and then what they derive from that, when they incorporate their data, which is their IP and create new models that's then their IP. So those are complicated questions and every company is a little different on what they're worried about with that. But many banks, we give them all the IP to make sure that they're comfortable, especially in financial services. But some other spaces, it's very competitive and they're not as worried about it because it's a known space. A lot of the algorithms we use are all open source. They're known algorithms. So there's not a lot of problem there. But it's how you apply them to get insights. That's exactly right, how you apply them in that boundary of what is IP, what's not, it gets kind of fuzzy. And we encourage our clients a lot of time to drive that for the organization. For us internally, GDPR readiness, it was occurring at the business unit level, functional area, we weren't where we needed to be in terms of achieving compliance and we as a studio office took ownership of that across the business and got it where we needed to be. And so we often encourage our clients to take ownership of something like that and use it as an opportunity to differentiate. Yeah, and I talk about the old-time clients. Their data is important to them. Training models with that data for some new, making new decisions is their unique value prop in their IP. So we encourage them to make sure they're aware of that. Don't just throw their data in any canned service out there, model, because they could be given away their intellectual property and it's important to understand that. So, I mean, that's a complicated one, right? The IP piece. And the other two seem to be even tougher in some regards, like the free flow of data. I can see a lot of governments not wanting the free flow of data, and then the client is in the middle. They're like, hey, the government is going to adjudicate. What's that conversation like? The example that he gave, or maybe it was Interpol, if it's information about baggage claims, you can use the blockchain, encrypt it, and then only see the data at the other end. So that was actually a good example. Why do you want to restrict that flow of data? But if it's personal information, keep it in country. How is that conversation going with clients? Those can involve dependent on the country, right? And where you're at in the industry. Yeah, but even some Western countries are pretty strict about that stuff. Absolutely, and this is why we've created a platform that allows for data virtualization. We use Kubernetes and technologies under the covers so that you can manage that in different locations. You can manage it across a hybrid of data centers or a hybrid of public cloud vendors, and it allows you to still have one business application and you can kind of do some of that separation and even separation of data. So there's an approach there, but you got to do a balance, you got to balance it between innovation, digital transformation, and how much you want to govern. So govern is important, but for some projects we may want to just quickly prototype. So there's a balance there too. Well that data virtualization tech is interesting as it gets to the other piece, which was prevent data localization mandates, but if there is a mandate and we know that some countries aren't going to relax that mandate, you have a technical solution for that. An architecture that will support that, yep. And that's a big investment for us right now and we're doing a lot of work in that space. Obviously, with Red Hat you saw our partnership or acquisition that. So that's been. Red Hat, yeah, I heard something about that. Yeah, that's important for us. That's a big part of chapter two. All right, we'll give you the final word, Caitlin, on the spring, I guess it's not spring, it's technically the summer CDO event. No, it's been a great first day. So we kicked off today, we've got a full set of client panels tomorrow. We've got some announcements around our metadata that I mentioned, risk insights is a really cool offering we'll be talking more about. We also have cognitive support. This is another one our clients said they really wanted to help with some of their support back in systems. So a lot of exciting announcements, new thought leadership coming out. It's been a great event and looking forward to the next day. Well I love the fact that you guys have tied data science into the C-suite role. You guys have done a great job, I think better than anybody in terms of really advocating for the chief data officer. And this is a great event because it's peers talking to peers, a lot of private conversations going on. So congratulations on all the success and continued success worldwide. Thank you so much, thank you Dave. All right, you're welcome. Keep it right there, everybody. We'll be back with our next guest. We're ready for this short break. We have a panel coming up. This is Dave Vellante, you're watching theCUBE from IBM CDO. We'll be right back.