>> live from San Francisco, California. It's the key. You covering the IBM chief Data officer? Someone brought to you by IBM. >> We're back at the IBM CDO conference. Fisherman's Worf Worf in San Francisco. You're watching the Cube, the leader in life tech coverage. My name is David Dante. Glenn Finches. Here's the global leader of Big Data Analytics and IBM, and we're pleased to have Mark Clare. He's the head of data enablement at AstraZeneca. Gentlemen, welcome to the Cube. Thanks for coming on my mark. I'm gonna start with this head of data Data Enablement. That's a title that I've never heard before. And I've heard many thousands of titles in the Cube. What is that all about? >> Well, I think it's the credit goes to some of the executives at AstraZeneca when they recruited me. I've been a cheap date officer. Several the major financial institutions, both in the U. S. And in Europe. Um, AstraZeneca wanted to focus on how we actually enable our business is our science areas in our business is so it's not unlike a traditional CDO role, but we focus a lot more on what the enabling functions or processes would be >> So it sounds like driving business value is really the me and then throw. Sorry. >> I've always looked at this role in three functions value, risk and cost. So I think that in any CDO role, you have to look at all three. I think the you'd slide it if you didn't. This one with the title. Obviously, we're looking at quite a bit at the value we will drive across the the firm on how to leverage our date in a different way. >> I love that because you can quantify all three. All right, Glenn. So you're the host of this event. So awesome. I love that little presentation that you gave. So for those you didn't see it, you gave us pay stubs and then you gave us a website and said, Take a picture of the paste up, uploaded, and then you showed how you're working with your clients. Toe. Actually digitize that and compress all kinds of things. Time to mortgage origination. Time to decision. So explain that a little bit. And what's that? What's the tech behind that? And how are people using it? You know, >> for three decades, we've had this OCR technology where you take a piece of paper, you tell the machine what's on the paper. What longitudinal Enter the coordinates are and you feed it into the hope and pray to God that it isn't in there wrong. The form didn't change anything like that. That's what that's way. We've lived for three decades with cognitive and a I, but I read things like the human eye reads things. And so you put the page in and the machine comes back and says, Hey, is this invoice number? Hey, is this so security number? That's how you train it as compared to saying, Here's what it So we use this cognitive digitization capability to grab data that's locked in documents, and then you bring it back to the process so that you can digitally re imagine the process. Now there's been a lot of use of robotics and things like that. I'm kind of taken existing processes, and I'm making them incrementally. Better write This says look, you now have the data of the process. You can re imagine it. However, in fact, the CEO of our client ADP said, Look, I want you to make me a Netflix, not a blood Urbach Blockbuster, right? So So it's a mind shift right to say we'll use this data will read it with a I will digitally re imagine the process. And it usually cuts like 70 or 80% of the cycle time, 50 to 75% of the cost. I mean, it's it's pretty groundbreaking when you see it. >> So markets ahead of data neighborhood. You hear something like that and you're not. You're not myopically focused on one little use case. You're taking a big picture of you doing strategies and trying to develop a broader business cases for the organization. But when you see an example like that and many examples out there, I'm sure the light bulbs go off. So >> I wrote probably 10 years cases down while >> Glenn was talking about you. You do get tactical, Okay, but but But where do you start when you're trying to solve these problems? >> Well, I look att, Glenn's example, And about five and 1/2 years ago, Glenn was one I went to had gone to a global financial service, firms on obviously having scale across dozens of countries, and I had one simple request. Thio Glenn's team as well as a number of other technology companies. I want cognitive intelligence for on data in Just because the process is we've had done for 20 years just wouldn't scale not not its speed across many different languages and cultures. And I now look five and 1/2 years later, and we have beginning of, I would say technology opportunities. When I asked Glenn that question, he was probably the only one that didn't think I had horns coming out of my head, that I was crazy. I mean, some of the leading technology firms thought I was crazy asking for cognitive data management capabilities, and we are five and 1/2 years later and we're seeing a I applied not just on the front end of analytics, but back in the back end of the data management processes themselves started automate. So So I look, you know, there's a concept now coming out day tops on date offices. You think of what Dev Ops is. It's bringing within our data management processes. It's bringing cognitive capabilities to every process step, And what level of automation can we do? Because the, you know, for typical data science experiment 80 to 90% of that work Estate engineering. If I can automate that, then through a date office process, then I could get to incite much faster, but not in scale it and scale a lot more opportunities and have to manually do it. So I I look at presentations and I think, you know, in every aspect of our business, where we clear could we apply >> what you talk about date engineering? You talk about data scientist spending his or her time just cleaning the wrangling data, All the all the not fun stuff exactly plugging in cables back in the infrastructure date. >> You're seeing horror stories right now. I heard from a major academic institution. A client came to them and their data scientists. They had spent several years building. We're spending 99% of their time trying to cleanse and prep data. They were spend 90% cleansing and prepping, and of the remaining 10% 90% of that fixing it where they fix it wrong and the first time so they had 1% of their job doing their job. So this is a huge opportunity. You can start automating more of that and actually refocusing data science on data >> science. So you've been a chief data officer number of financial institutions. You've got this kind of cool title now, which touches on some of the things a CDO might do and your technical. We got a technical background. So when you look a lot of the what Ginny Rometty calls incumbents, call them incumbent Disruptors two years ago at Ivy and think they've got data that has been hardened, you know, in all these projects and use cases and it's locked and people talk about the silos, part of your role is to figure out Okay, how do we get that data out? Leverage. It put it at the core. Is that is that fair? >> Well, and I'm gonna stay away from the word core cause to make core Kenan for kind of legacy processes of building a single repositories single warehouse, which is very time consuming. So I think I can I leave it where it is, but find a wayto to unify it. >> Not physically, exactly what I say. Corny, but actually the court, that's what we need >> to think about is how to do this logically and cream or of Ah unification approach that has speed and agility with it versus the old physical approaches, which took time. And resource is >> so That's a that's a computer science problem that people have been trying to solve for years. Decentralized, distributed, dark detectors, right? And why is it that we're now able Thio Tap your I think it's >> a perfect storm of a I of Cloud, the cloud native of Io ti, because when you think of I o. T, it's a I ot to be successful fabric that can connect millions of devices or millions of sensors. So you'd be paired those three with the investment big data brought in the last seven or eight years and big data to me. Initially, when I started talking to companies in the Valley 10 years ago, the early days of, um, apparatus, what I saw or companies and I could get almost any of the digital companies in the valley they were not. They were using technology to be more agile. They were finding agile data science. Before we call the data signs the map produce and Hadoop, we're just and after almost not an afterthought. But it was just a mechanism to facilitate agility and speed. And so if you look at how we built out all the way up today and all the convergence of all these new technologies, it's a perfect storm to actually innovate differently. >> Well, what was profound about my producing in the dupe? It was like leave the data where it is and shipped five megabytes a code two upended by the data and that you bring up a good point. We've now, we spent 10 years leveraging that at a much lower cost. And you've got the cloud now for scale. And now machine intelligence comes in that you can apply in the data causes. Bob Pityana once told me, Data's plentiful insights aren't Amen to that. So Okay, so this is really interesting discussion. You guys have known each other for a couple of couple of decades. How do you work together toe to solve problems Where what is that conversation like, Do >> you want to start that? >> So, um, first of all, we've never worked together on solving small problems, not commodity problems. We would usually tackle something that someone would say would not be possible. So normally Mark is a change agent wherever he goes. And so he usually goes to a place that wants to fix something or change something in an abnormally short amount of time for an abnormally small amount of money. Right? So what's strange is that we always find that space together. Mark is very judicious about using us as a service is firm toe help accelerate those things. But then also, we build in a plan to transition us away in transition, in him into full ownership. Right. But we usually work together to jump start one of these wicked, hard, wicked, cool things that nobody else >> was. People hate you. At first. They love you. I would end the one >> institution and on I said, OK, we're going to a four step plan. I'm gonna bring the consultants in day one while we find Thailand internally and recruit talent External. That's kind of phases one and two in parallel. And then we're gonna train our talent as we find them, and and Glenn's team will knowledge transfer, and by face for where, Rayna. And you know, that's a model I've done successfully in several organizations. People can. I hated it first because they're not doing it themselves, but they may not have the experience and the skills, and I think as soon as you show your staff you're willing to invest in them and give them the time and exposure. The conversation changes, but it's always a little awkward. At first, I've run heavy attrition, and some organizations at first build the organizations. But the one instance that Glen was referring to, we came in there and they had a 4 1 1 2 1 12 to 15 year plan and the C I O. Looked at me, he says. I'll give you two years. I'm a bad negotiator. I got three years out of it and I got a business case approved by the CEO a week later. It was a significant size business case in five minutes. I didn't have to go back a second or third time, but we said We're gonna do it in three years. Here's how we're gonna scale an organization. We scaled more than 1000 person organization in three years of talent, but we did it in a planned way and in that particular organization, probably a year and 1/2 in, I had a global map of every data and analytics role I need and I could tell you were in the US they set and with what competitors earning what industry and where in India they set and in what industry And when we needed them. We went out and recruited, but it's time to build that. But you know, in any really period, I've worked because I've done this 20 plus years. The talent changes. The location changes someone, but it's always been a challenge to find him. >> I guess it's good to have a deadline. I guess you did not take the chief data officer role in your current position. Explain that. What's what. What's your point of view on on that role and how it's evolved and how it's maybe being used in ways that don't I >> mean, I think that a CDO, um on during the early days, there wasn't a definition of a matter of fact. Every time I get a recruiter, call me all. We have a great CDO row for first time I first thing I asked him, How would you define what you mean by CDO? Because I've never seen it defined the same way into cos it's just that way But I think that the CDO, regardless of institutions, responsibility end in to make sure there's an Indian framework from strategy execution, including all of the governance and compliance components, and that you have ownership of each piece in the organization. CDO most companies doesn't own all of that, but I think they have a responsibility and too many organizations that hasn't occurred. So you always find gaps and each organization somewhere between risk costs and value, in terms of how how they're, how the how the organization's driving data and in my current role. Like I said, I wanted to focus. We want the focus to really be on how we're enabling, and I may be enabling from a risk and compliance standpoint, Justus greatly as I'm enabling a gross perspective on the business or or cost management and cost reductions. We have been successful in several programs for self funding data programs for multi gears. By finding and costs, I've gone in tow several organizations that it had a decade of merger after merger and Data's afterthought in almost any merger. I mean, there's a Data Silas section session tomorrow. It'd be interesting to sit through that because I've found that data data is the afterthought in a lot of mergers. But yet I knew of one large health care company. They've made data core to all of their acquisitions, and they was one the first places they consolidated. And they grew faster by acquisition than any of their competitors. So I think there's a There's a way to do it correctly. But in most companies you go in, you'll find all kinds of legacy silos on duplication, and those are opportunities to, uh, to find really reduce costs and self fund. All the improvements, all the strategic programs you wanted, >> a number inferring from the Indian in the data roll overlaps or maybe better than gaps and data is that thread between cost risk. And it is >> it is. And I've been lucky in my career. I've report toe CEOs. I reported to see Yellows, and I've reported to CEO, so I've I've kind of reported in three different ways, and each of those executives really looked at it a little bit differently. Value obviously is in a CEO's office, you know, compliance. Maurizio owes office and costs was more in the c i o domain, but you know, we had to build a program looking >> at all three. >> You know, I think this topic, though, that we were just talking about how these rules are evolving. I think it's it's natural, because were about 5 2.0. to 7 years into the evolution of the CDO, it might be time for a CDO Um, and you see Maur CEOs moving away from pure policy and compliance Tomb or value enablement. It's a really hard change, and that's why you're starting to Seymour turnover of some of the studios because people who are really good CEOs at policy and risk and things like that might not be the best enablers, right? So I think it's pretty natural evolution. >> Great discussion, guys. We've got to leave it there, They say. Data is the new oil date is more valuable than oil because you could use data to reduce costs to reduce risk. The same data right toe to drive revenue, and you can't put a gallon of oil in your car and a quart of oil in the car quarter in your house of data. We think it's even more valuable. Gentlemen, thank you so much for coming on the cues. Thanks so much. Lot of fun. Thanks. Keep right, everybody. We'll be back with our next guest. You're watching the Cube from IBM CDO 2019 right back.

>> live from San Francisco, California. It's the Q covering the IBM Chief Data Officer Summit brought to you by IBM. >> Welcome back to Fisherman's Fisherman's Wharf in San Francisco. Everybody, my name is David wanted. You're watching the Cube, the leader in live tech coverage, you ought to events. We extract the signal from the noise. We're here. The IBM CDO event. This is the 10th anniversary of this event. Caitlin Hallford is here. She's the director of a I Accelerator and client success at IBM. Caitlin, great to see you again. Wow. 10 years. Amazing. They and Carlo Apple Apple Glace e is here. Who is the program director for data and a I at IBM. Because you again, my friend. Thanks for coming on to Cuba. Lums. Wow, this is 10 years, and I think the Cube is covered. Probably eight of these now. Yeah, kind of. We bounce between San Francisco and Boston to great places for CEOs. Good places to have intimate events, but and you're taking it global. I understand. Congratulations. Congratulations on the promotion. Thank you. Going. Thank you so much. >> So we, as you know well are well, no. We started our chief date officer summits in San Francisco here, and it's gone 2014. So this is our 10th 1 We do two a year. We found we really have a unique cohort of clients. The join us about 100 40 in San Francisco on the spring 140 in Boston in the fall, and we're here celebrating the 10th 10 Summit. >> So, Carlo, talk about your role and then let's get into how you guys, you know, work together. How you hand the baton way we'll get to the client piece. >> So I lead the Data Center League team, which is a group within our product development, working side by side with clients really to understand their needs as well developed, 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 CEO team on best practices, what patterns they're seeing from an architecture perspective. Make sure that our platforms really incorporating that stuff. >> And if I recall the data science that lead team is its presales correct and could >> be posted that it could, 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? So it can be a for pay service. Is that correct or Yeah, we can >> before pay. Or sometimes we do it based on just our relation with >> It's kind of a mixed then. Right? Okay, so you're learning the client's learning, so they're obviously good, good customers. And so you want to treat him right >> now? How do you guys work >> together? Maybe Caitlin, you can explain. The two organizations >> were often the early testers, early adopters of some of the capabilities. And so what we'll do is we'll test will literally will prove it out of skill 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 in a product and drive that into market, and we share a lot of client relationships where CEOs come to us, they're want advice and counsel on best practices across the organization. And they're looking for latest applications to deploy deploy known environments and so we can capture a lot of that feedback in some of the market user testing proved that out. Using IBM is an example and then work with you to really commercialized and bring it to market in the most efficient manner. >> You were talking this morning. You had a picture up of the first CDO event. No Internet, no wife in the basement. I love it. So how is this evolved from a theme standpoint? What do you What are the patterns? Sure. So when >> we started this, it was really a response. Thio primarily financial service is sector regulatory requirements, trying to get data right to meet those regulatory compliance initiatives. Defensive posture certainly weren't driving transformation within their enterprises. And what I've seen is a couple of those core elements are still key for us or data governance and data management. And some of those security access controls are always going to be important. But we're finding his videos more and more, have expanded scope of responsibilities with the enterprise they're looked at as a leader. They're no longer sitting within a c i o function there either appear or, you know, working in partnership with, and they're driving enterprise wide, you know, initiatives for the for their enterprises and organizations, which has been great to see. >> So we all remember when you know how very and declared data science was gonna be the number one job, and it actually kind of has become. I think I saw somewhere, maybe in Glass door 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 these blueprints, you're now applying them, accelerating different industries. You mentioned health care this morning. >> What are some >> of those industry accelerators And how is that actually coming to fruition? Yes. >> So some of the things we're seeing is speaking of financial clients way go into a lot of them. We do these one on one engagements, we build them from custom. We co create these engineering solutions, 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 Aye, aye. It's difficult to develop a solution because everybody's date is different. Everybody's business is different. So what we're trying to do is build these. We can't just build a widget that's going to solve the 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. But these accelerators, which are a set of core code source code notebooks, industry models in terms a CZ wells dashboards that allow them to quickly build out these use cases around a turn or segmentation on dhe. You know some other models we can grab 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 an organization. That's kind of what we're doing. >> You prime the pump >> prime minute pump, we call them there right now, we're doing client in eights for wealth management, and we're doing that, ref SS. And they come right on the box of our cloudpack for data platform. You could quickly click and install button, and in there you'll get the sample data files. You get no books. You get industry terms, your governance capability, as well as deployed dashboards and models. >> So talk more about >> cloudpack for data. What's inside of that brought back the >> data is a collection of micro Service's Andi. It includes a lot of things that we bring to market to help customers with their journey things from like data ingestion collection to all the way Thio, eh? I model development from building your models to deploying them to actually infusing them in your business process with bias detection or integration way have a lot of capability. Part >> of it's actually tooling. It's not just sort of so how to Pdf >> dualism entire platform eso. So the platform itself has everything you need an organization to kind of go from an idea to data ingestion and governance and management all the way to model training, development, deployment into integration into your business process. >> Now Caitlin, in the early days of the CDO, saw CDO emerging in healthcare, financialservices and government. And now it's kind of gone mainstream to the point where we had Mark Clare on who's the head of data neighborhood AstraZeneca. And he said, I'm not taking the CDO title, you know, because I'm all about data enablement and CDO. You know, title has sort of evolved. What have you seen? It's got clearly gone mainstream Yep. What are you seeing? In terms of adoption of that, that role and its impact on organizations, >> So couple of transit has been interesting both domestically and internationally as well. So we're seeing a lot of growth outside of the U. S. So we did our first inaugural summit in Tokyo. In Japan, there's a number of day leaders in Japan that are really eager to jump start their transformation initiatives. Also did our first Dubai summit. Middle East and Africa will be in South Africa next month at another studio summit. And what I'm seeing is outside of North America a lot of activity and interest in creating an enabling studio light capability. Data Leader, Like, um, and some of these guys, I think we're gonna leapfrog ahead. I think they're going to just absolutely jump jump ahead and in parallel, those traditional industries, you know, there's a new federal legislation coming down by year end for most federal agencies to appoint a chief data officer. So, you know, Washington, D. C. Is 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, you know, outside the U. S. And cross nontraditional, >> you say >> Jump ahead. You mean jump ahead of where maybe some of the U. S. >> Absolute best? Absolutely. And I'm >> seeing a trend where you know, a lot of CEOs they're 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 there is really an important role in every organization that I'm seeing for every client I go to. It's a little different, but you're right, it's it's definitely up and coming. Role is very important for especially for digital transformation. >> This is so good. I was gonna say one of the ways they are teens really, partner Well, together, I think is weaken source some of these in terms of enabling that you know, acceleration and leap frog. What are those pain points or use cases in traditional data management space? You know, the metadata. So I think you talk with Steven earlier about how we're doing some automated meditate a generation and really using a i t. O 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. Carlos using. And our clients are saying, Hey, we're spending, you know, 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, adopts something like that, press internally and then work with you guys >> actually think of every detail developer out there that has to go figure out what this date is. If you have a tool which we're trying to cooperate the platform based on the guidance from the CDO Global CEO team, we can automatically create that metadata are likely ingested and provide into platform so that data scientists can start to get value out >> of it quickly. So we heard Martin Schroeder talked about 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, yeah, and then protect algorithms and source code, which is an I P protection thing. So I'm interested in how your customers air Reacting to that framework, I presume the protect the algorithms and source code I p. That's near and dear right? 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 I p? You know, how do we manage this? And you know, what we bring to the table with the accelerators is to help them jump start them right, even though that it's kind of our a p we created, but we give it to them and then what they derive from that when they incorporate their data, which is their i p, and create new models, that is then their i. P. So those air complicated questions and every company is a little different on what they're worried about with that, so but many banks, we give them all the I P to make sure that they're comfortable and especially in financial service is but some other spaces. It's very competitive. And then I was worried about it because it's, ah, known space. A lot of the algorithm for youse are all open source. They're known algorithms, so there's not a lot of problem there. >> It's how you apply them. That's >> exactly right how you apply them in that boundary of what >> is P, What's not. It's kind of >> fuzzy, >> and we encourage our clients a lot of times to drive that for >> the >> organisation, for us, internally, GDP, our readiness, it was occurring to the business unit level functional area. So it was, you know, we weren't where we needed to be in terms of achieving compliance. And we have the CEO 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. >> And I talked about the whole time of clients. Their data is impor onto them. Them training models with that data for some new making new decisions is their unique value. Prop In there, I'd be so so we encourage them to make sure they're aware that don't just tore their data in any can, um, service out there model because they could be giving away their intellectual property, and it's important. Didn't understand that. >> So that's a complicated one. Write the piece and the other two seem to be even tougher. And some regards, like the free flow of data. I could see a lot of governments not wanting the free flow of data, but and the client is in the middle. OK, d'oh. Government is gonna adjudicate. What's that conversation like? The example that he gave was, maybe was interpolate. If it's if it's information about baggage claims, you can you can use the Blockchain and crypt it and then only see the data at the other end. So that was actually, I thought, a good example. Why do you want to restrict that flow of data? But if it's personal information, keep it in country. But how is that conversation going with clients? >> Leo. Those can involve depending on the country, right and where you're at in the industry. >> But some Western countries are strict about that. >> Absolutely. And this is why we've created a platform that allows for data virtualization. We use Cooper nannies and technologies under the covers so that you can manage that in different locations. You could manage it across. Ah, hybrid of data centers or hybrid of public cloud vendors. And it allows you to still have one business application, and you can kind of do some of the separation and even separation of data. So there's there's, there's, there's an approach there, you know. But you gotta do a balance. Balance it. You gotta balance between innovation, digital transformation and how much you wanna, you know, govern so governs important. And then, you know. 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 because it gets 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, ah, a technical solution for that >> architecture that will support that. And that's a big investment for us right now. And where we're doing a lot of work in that space. Obviously, with red hat, you saw partnership or acquisition. So that's been >> really Yeah, I heard something about that's important. That's that's that's a big part of Chapter two. Yeah, all right. We'll give you the final world Caitlyn on the spring. I guess it's not spring it. Secondly, this summer, right? CDO event? >> No, it's been agreed. First day. So we kicked off. Today. We've got a full set of client panel's tomorrow. We've got some announcements around our meta data 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 that I 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 next day. >> Well, I love the fact >> that you guys have have tied data science into the sea. Sweet roll. You guys have done a great job, I think, better than anybody in terms of of, of really advocating for the chief data officer. And this is a great event because it's piers talking. Appears a lot of private conversations going on. So congratulations on all the success and continued success worldwide. >> Thank you so much. Thank you, Dave. >> You welcome. Keep it right there, everybody. We'll be back with our next guest. Ready for this short break. We have a panel coming up. This is David. Dante. You're >> watching the Cube from IBM CDO right back.