>> Live from San Francisco, California, it's theCUBE covering the IBM Chief Data Officers Summit, brought to you by IBM. >> Welcome back to San Francisco everybody. I'm Dave Vellante and you're watching theCUBE, the leader in live tech coverage. This is the end of the day panel at the IBM Chief Data Officer Summit. This is the 10th CDO event that IBM has held and we love to to gather these panels. This is a data all-star panel and I've recruited Seth Dobrin who is the CDO of the analytics group at IBM. Seth, thank you for agreeing to chip in and be my co-host in this segment. >> Yeah, thanks Dave. Like I said before we started, I don't know if this is a promotion or a demotion. (Dave laughing) >> We'll let you know after the segment. So, the data all-star panel and the data all-star awards that you guys are giving out a little later in the event here, what's that all about? >> Yeah so this is our 10th CDU Summit. So two a year, so we've been doing this for 5 years. The data all-stars are those people that have been to four at least of the ten. And so these are five of the 16 people that got the award. And so thank you all for participating and I attended these like I said earlier, before I joined IBM they were immensely valuable to me and I was glad to see 16 other people that think it's valuable too. >> That is awesome. Thank you guys for coming on. So, here's the format. I'm going to introduce each of you individually and then ask you to talk about your role in your organization. What role you play, how you're using data, however you want to frame that. And the first question I want to ask is, what's a good day in the life of a data person? Or if you want to answer what's a bad day, that's fine too, you choose. So let's start with Lucia Mendoza-Ronquillo. Welcome, she's the Senior Vice President and the Head of BI and Data Governance at Wells Fargo. You told us that you work within the line of business group, right? So introduce your role and what's a good day for a data person? >> Okay, so my role basically is again business intelligence so I support what's called cards and retail services within Wells Fargo. And I also am responsible for data governance within the business. We roll up into what's called a data governance enterprise. So we comply with all the enterprise policies and my role is to make sure our line of business complies with data governance policies for enterprise. >> Okay, good day? What's a good day for you? >> A good day for me is really when I don't get a call that the regulators are knocking on our doors. (group laughs) Asking for additional reports or have questions on the data and so that would be a good day. >> Yeah, especially in your business. Okay, great. Parag Shrivastava is the Director of Data Architecture at McKesson, welcome. Thanks so much for coming on. So we got a healthcare, couple of healthcare examples here. But, Parag, introduce yourself, your role, and then what's a good day or if you want to choose a bad day, be fun the mix that up. >> Yeah, sounds good. Yeah, so mainly I'm responsible for the leader strategy and architecture at McKesson. What that means is McKesson has a lot of data around the pharmaceutical supply chain, around one-third of the world's pharmaceutical supply chain, clinical data, also around pharmacy automation data, and we want to leverage it for the better engagement of the patients and better engagement of our customers. And my team, which includes the data product owners, and data architects, we are all responsible for looking at the data holistically and creating the data foundation layer. So I lead the team across North America. So that's my current role. And going back to the question around what's a good day, I think I would say the good day, I'll start at the good day. Is really looking at when the data improves the business. And the first thing that comes to my mind is sort of like an example, of McKesson did an acquisition of an eight billion dollar pharmaceutical company in Europe and we were creating the synergy solution which was based around the analytics and data. And actually IBM was one of the partners in implementing that solution. When the solution got really implemented, I mean that was a big deal for me to see that all the effort that we did in plumbing the data, making sure doing some analytics, is really helping improve the business. I think that is really a good day I would say. I mean I wouldn't say a bad day is such, there are challenges, constant challenges, but I think one of the top priorities that we are having right now is to deal with the demand. As we look at the demand around the data, the role of data has got multiple facets to it now. For example, some of the very foundational, evidentiary, and compliance type of needs as you just talked about and then also profitability and the cost avoidance and those kind of aspects. So how to balance between that demand is the other aspect. >> All right good. And we'll get into a lot of that. So Carl Gold is the Chief Data Scientist at Zuora. Carl, tell us a little bit about Zuora. People might not be as familiar with how you guys do software for billing et cetera. Tell us about your role and what's a good day for a data scientist? >> Okay, sure, I'll start by a little bit about Zuora. Zuora is a subscription management platform. So any company who wants to offer a product or service as subscription and you don't want to build your billing and subscription management, revenue recognition, from scratch, you can use a product like ours. I say it lets anyone build a telco with a complicated plan, with tiers and stuff like that. I don't know if that's a good thing or not. You guys'll have to make up your own mind. My role is an interesting one. It's split, so I said I'm a chief data scientist and we work about 50% on product features based on data science. Things like churn prediction, or predictive payment retries are product areas where we offer AI-based solutions. And then but because Zuora is a subscription platform, we have an amazing set of data on the actual performance of companies using our product. So a really interesting part of my role has been leading what we call the subscription economy index and subscription economy benchmarks which are reports around best practices for subscription companies. And it's all based off this amazing dataset created from an anonymized data of our customers. So that's a really exciting part of my role. And for me, maybe this speaks to our level of data governance, I might be able to get some tips from some of my co-panelists, but for me a good day is when all the data for me and everyone on my team is where we left it the night before. And no schema changes, no data, you know records that you were depending on finding removed >> Pipeline failures. >> Yeah pipeline failures. And on a bad day is a schema change, some crucial data just went missing and someone on my team is like, "The code's broken." >> And everybody's stressed >> Yeah, so those are bad days. But, data governance issues maybe. >> Great, okay thank you. Jung Park is the COO of Latitude Food Allergy Care. Jung welcome. >> Yeah hi, thanks for having me and the rest of us here. So, I guess my role I like to put it as I'm really the support team. I'm part of the support team really for the medical practice so, Latitude Food Allergy Care is a specialty practice that treats patients with food allergies. So, I don't know if any of you guys have food allergies or maybe have friends, kids, who have food allergies, but, food allergies unfortunately have become a lot more prevalent. And what we've been able to do is take research and data really from clinical trials and other research institutions and really use that from the clinical trial setting, back to the clinical care model so that we can now treat patients who have food allergies by using a process called oral immunotherapy. It's fascinating and this is really personal to me because my son as food allergies and he's been to the ER four times. >> Wow. >> And one of the scariest events was when he went to an ER out of the country and as a parent, you know you prepare your child right? With the food, he takes the food. He was 13 years old and you had the chaperones, everyone all set up, but you get this call because accidentally he ate some peanut, right. And so I saw this unfold and it scared me so much that this is something I believe we just have to get people treated. So this process allows people to really eat a little bit of the food at a time and then you eat the food at the clinic and then you go home and eat it. Then you come back two weeks later and then you eat a little bit more until your body desensitizes. >> So you build up that immunity >> Exactly. >> and then you watch the data obviously. >> Yeah. So what's a good day for me? When our patients are done for the day and they have a smile on their face because they were able to progress to that next level. >> Now do you have a chief data officer or are you the de facto CFO? >> I'm the de facto. So, my career has been pretty varied. So I've been essentially chief data officer, CIO, at companies small and big. And what's unique about I guess in this role is that I'm able to really think about the data holistically through every component of the practice. So I like to think of it as a patient journey and I'm sure you guys all think of it similarly when you talk about your customers, but from a patient's perspective, before they even come in, you have to make sure the data behind the science of whatever you're treating is proper, right? Once that's there, then you have to have the acquisition part. How do you actually work with the community to make sure people are aware of really the services that you're providing? And when they're with you, how do you engage them? How do you make sure that they are compliant with the process? So in healthcare especially, oftentimes patients don't actually succeed all the way through because they don't continue all the way through. So it's that compliance. And then finally, it's really long-term care. And when you get the long-term care, you know that the patient that you've treated is able to really continue on six months, a year from now, and be able to eat the food. >> Great, thank you for that description. Awesome mission. Rolland Ho is the Vice President of Data and Analytics at Clover Health. Tell us a little bit about Clover Health and then your role. >> Yeah, sure. So Clover is a startup Medicare Advantage plan. So we provide Medicare, private Medicare to seniors. And what we do is we're because of the way we run our health plan, we're able to really lower a lot of the copay costs and protect seniors against out of pocket. If you're on regular Medicare, you get cancer, you have some horrible accident, your out of pocket is infinite potentially. Whereas with Medicare Advantage Plan it's limited to like five, $6,000 and you're always protected. One of the things I'm excited about being at Clover is our ability to really look at how can we bring the value of data analytics to healthcare? Something I've been in this industry for close to 20 years at this point and there's a lot of waste in healthcare. And there's also a lot of very poor application of preventive measures to the right populations. So one of the things that I'm excited about is that with today's models, if you're able to better identify with precision, the right patients to intervene with, then you fundamentally transform the economics of what can be done. Like if you had to pa $1,000 to intervene, but you were only 20% of the chance right, that's very expensive for each success. But, now if your model is 60, 70% right, then now it opens up a whole new world of what you can do. And that's what excites me. In terms of my best day? I'll give you two different angles. One as an MBA, one of my best days was, client calls me up, says, "Hey Rolland, you know, "your analytics brought us over $100 million "in new revenue last year." and I was like, cha-ching! Excellent! >> Which is my half? >> Yeah right. And then on the data geek side the best day was really, run a model, you train a model, you get ridiculous AUC score, so area under the curve, and then you expect that to just disintegrate as you go into validation testing and actual live production. But the 98 AUC score held up through production. And it's like holy cow, the model actually works! And literally we could cut out half of the workload because of how good that model was. >> Great, excellent, thank you. Seth, anything you'd add to the good day, bad day, as a CDO? >> So for me, well as a CDO or as CDO at IBM? 'Cause at IBM I spend most of my time traveling. So a good day is a day I'm home. >> Yeah, when you're not in an (group laughing) aluminum tube. >> Yeah. Hurdling through space (laughs). No, but a good day is when a GDPR compliance just happened, a good day for me was May 20th of last year when IBM was done and we were, or as done as we needed to be for GDPR so that was a good day for me last year. This year is really a good day is when we start implementing some new models to help IBM become a more effective company and increase our bottom line or increase our margins. >> Great, all right so I got a lot of questions as you know and so I want to give you a chance to jump in. >> All right. >> But, I can get it started or have you got something? >> I'll go ahead and get started. So this is a the 10th CDO Summit. So five years. I know personally I've had three jobs at two different companies. So over the course of the last five years, how many jobs, how many companies? Lucia? >> One job with one company. >> Oh my gosh you're boring. (group laughing) >> No, but actually, because I support basically the head of the business, we go into various areas. So, we're not just from an analytics perspective and business intelligence perspective and of course data governance, right? It's been a real journey. I mean there's a lot of work to be done. A lot of work has been accomplished and constantly improving the business, which is the first goal, right? Increasing market share through insights and business intelligence, tracking product performance to really helping us respond to regulators (laughs). So it's a variety of areas I've had to be involved in. >> So one company, 50 jobs. >> Exactly. So right now I wear different hats depending on the day. So that's really what's happening. >> So it's a good question, have you guys been jumping around? Sure, I mean I think of same company, one company, but two jobs. And I think those two jobs have two different layers. When I started at McKesson I was a solution leader or solution director for business intelligence and I think that's how I started. And over the five years I've seen the complete shift towards machine learning and my new role is actually focused around machine learning and AI. That's why we created this layer, so our own data product owners who understand the data science side of things and the ongoing and business architecture. So, same company but has seen a very different shift of data over the last five years. >> Anybody else? >> Sure, I'll say two companies. I'm going on four years at Zuora. I was at a different company for a year before that, although it was kind of the same job, first at the first company, and then at Zuora I was really focused on subscriber analytics and churn for my first couple a years. And then actually I kind of got a new job at Zuora by becoming the subscription economy expert. I become like an economist, even though I don't honestly have a background. My PhD's in biology, but now I'm a subscription economy guru. And a book author, I'm writing a book about my experiences in the area. >> Awesome. That's great. >> All right, I'll give a bit of a riddle. Four, how do you have four jobs, five companies? >> In five years. >> In five years. (group laughing) >> Through a series of acquisition, acquisition, acquisition, acquisition. Exactly, so yeah, I have to really, really count on that one (laughs). >> I've been with three companies over the past five years and I would say I've had seven jobs. But what's interesting is I think it kind of mirrors and kind of mimics what's been going on in the data world. So I started my career in data analytics and business intelligence. But then along with that I had the fortune to work with the IT team. So the IT came under me. And then after that, the opportunity came about in which I was presented to work with compliance. So I became a compliance officer. So in healthcare, it's very interesting because these things are tied together. When you look about the data, and then the IT, and then the regulations as it relates to healthcare, you have to have the proper compliance, both internal compliance, as well as external regulatory compliance. And then from there I became CIO and then ultimately the chief operating officer. But what's interesting is as I go through this it's all still the same common themes. It's how do you use the data? And if anything it just gets to a level in which you become closer with the business and that is the most important part. If you stand alone as a data scientist, or a data analyst, or the data officer, and you don't incorporate the business, you alienate the folks. There's a math I like to do. It's different from your basic math, right? I believe one plus one is equal to three because when you get the data and the business together, you create that synergy and then that's where the value is created. >> Yeah, I mean if you think about it, data's the only commodity that increases value when you use it correctly. >> Yeah. >> Yeah so then that kind of leads to a question that I had. There's this mantra, the more data the better. Or is it more of an Einstein derivative? Collect as much data as possible but not too much. What are your thoughts? Is more data better? >> I'll take it. So, I would say the curve has shifted over the years. Before it used to be data was the bottleneck. But now especially over the last five to 10 years, I feel like data is no longer oftentimes the bottleneck as much as the use case. The definition of what exactly we're going to apply to, how we're going to apply it to. Oftentimes once you have that clear, you can go get the data. And then in the case where there is not data, like in Mechanical Turk, you can all set up experiments, gather data, the cost of that is now so cheap to experiment that I think the bottleneck's really around the business understanding the use case. >> Mm-hmm. >> Mm-hmm. >> And I think the wave that we are seeing, I'm seeing this as there are, in some cases, more data is good, in some cases more data is not good. And I think I'll start it where it is not good. I think where quality is more required is the area where more data is not good. For example like regulation and compliance. So for example in McKesson's case, we have to report on opioid compliance for different states. How much opioid drugs we are giving to states and making sure we have very, very tight reporting and compliance regulations. There, highest quality of data is important. In our data organization, we have very, very dedicated focus around maintaining that quality. So, quality is most important, quantity is not if you will, in that case. Having the right data. Now on the other side of things, where we are doing some kind of exploratory analysis. Like what could be a right category management for our stores? Or where the product pricing could be the right ones. Product has around 140 attributes. We would like to look at all of them and see what patterns are we finding in our models. So there you could say more data is good. >> Well you could definitely see a lot of cases. But certainly in financial services and a lot of healthcare, particularly in pharmaceutical where you don't want work in process hanging around. >> Yeah. >> Some lawyer could find a smoking gun and say, "Ooh see." And then if that data doesn't get deleted. So, let's see, I would imagine it's a challenge in your business, I've heard people say, "Oh keep all the, now we can keep all the data, "it's so inexpensive to store." But that's not necessarily such a good thing is it? >> Well, we're required to store data. >> For N number of years, right? >> Yeah, N number of years. But, sometimes they go beyond those number of years when there's a legal requirements to comply or to answer questions. So we do keep more than, >> Like a legal hold for example. >> Yeah. So we keep more than seven years for example and seven years is the regulatory requirement. But in the case of more data, I'm a data junkie, so I like more data (laughs). Whenever I'm asked, "Is the data available?" I always say, "Give me time I'll find it for you." so that's really how we operate because again, we're the go-to team, we need to be able to respond to regulators to the business and make sure we understand the data. So that's the other key. I mean more data, but make sure you understand what that means. >> But has that perspective changed? Maybe go back 10 years, maybe 15 years ago, when you didn't have the tooling to be able to say, "Give me more data." "I'll get you the answer." Maybe, "Give me more data." "I'll get you the answer in three years." Whereas today, you're able to, >> I'm going to go get it off the backup tapes (laughs). >> (laughs) Yeah, right, exactly. (group laughing) >> That's fortunately for us, Wells Fargo has implemented data warehouse for so many number of years, I think more than 10 years. So we do have that capability. There's certainly a lot of platforms you have to navigate through, but if you are able to navigate, you can get to the data >> Yeah. >> within the required timeline. So I have, astonished you have the technology, team behind you. Jung, you want to add something? >> Yeah, so that's an interesting question. So, clearly in healthcare, there is a lot of data and as I've kind of come closer to the business, I also realize that there's a fine line between collecting the data and actually asking our folks, our clinicians, to generate the data. Because if you are focused only on generating data, the electronic medical records systems for example. There's burnout, you don't want the clinicians to be working to make sure you capture every element because if you do so, yes on the back end you have all kinds of great data, but on the other side, on the business side, it may not be necessarily a productive thing. And so we have to make a fine line judgment as to the data that's generated and who's generating that data and then ultimately how you end up using it. >> And I think there's a bit of a paradox here too, right? The geneticist in me says, "Don't ever throw anything away." >> Right. >> Right? I want to keep everything. But, the most interesting insights often come from small data which are a subset of that larger, keep everything inclination that we as data geeks have. I think also, as we're moving in to kind of the next phase of AI when you can start doing really, really doing things like transfer learning. That small data becomes even more valuable because you can take a model trained on one thing or a different domain and move it over to yours to have a starting point where you don't need as much data to get the insight. So, I think in my perspective, the answer is yes. >> Yeah (laughs). >> Okay, go. >> I'll go with that just to run with that question. I think it's a little bit of both 'cause people touched on different definitions of more data. In general, more observations can never hurt you. But, more features, or more types of things associated with those observations actually can if you bring in irrelevant stuff. So going back to Rolland's answer, the first thing that's good is like a good mental model. My PhD is actually in physical science, so I think about physical science, where you actually have a theory of how the thing works and you collect data around that theory. I think the approach of just, oh let's put in 2,000 features and see what sticks, you know you're leaving yourself open to all kinds of problems. >> That's why data science is not democratized, >> Yeah (laughing). >> because (laughing). >> Right, but first Carl, in your world, you don't have to guess anymore right, 'cause you have real data. >> Well yeah, of course, we have real data, but the collection, I mean for example, I've worked on a lot of customer churn problems. It's very easy to predict customer churn if you capture data that pertains to the value customers are receiving. If you don't capture that data, then you'll never predict churn by counting how many times they login or more crude measures of engagement. >> Right. >> All right guys, we got to go. The keynotes are spilling out. Seth thank you so much. >> That's it? >> Folks, thank you. I know, I'd love to carry on, right? >> Yeah. >> It goes fast. >> Great. >> Yeah. >> Guys, great, great content. >> Yeah, thanks. And congratulations on participating and being data all-stars. >> We'd love to do this again sometime. All right and thank you for watching everybody, it's a wrap from IBM CDOs, Dave Vellante from theCUBE. We'll see you next time. (light music)

>> 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.

>> Live from San Francisco, California, it's theCUBE covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> Hi everybody, welcome back to theCUBE's coverage of the IBM Chief Data Officer Event. We're here at Fisherman's Wharf in San Francisco at the Centric Hyatt Hotel. This is the 10th anniversary of IBM's Chief Data Officer Summits. In the recent years, anyway, they do one in San Francisco and one in Boston each year, and theCUBE has covered a number of them. I think this is our eighth CDO conference. I'm Dave Vellante, and theCUBE, we like to go out, especially to events like this that are intimate, there's about 140 chief data officers here. We've had the chief data officer from AstraZeneca on, even though he doesn't take that title. We've got a panel coming up later on in the day. And I want to talk about the evolution of that role. The chief data officer emerged out of kind of a wonky, back-office role. It was all about 10, 12 years ago, data quality, master data management, governance, compliance. And as the whole big data meme came into focus and people were realizing that data is the new source of competitive advantage, that data was going to be a source of innovation, what happened was that role emerged, that CDO, chief data officer role, emerged out of the back office and came right to the front and center. And the chief data officer really started to better understand and help companies understand how to monetize the data. Now monetization of data could mean more revenue. It could mean cutting costs. It could mean lowering risk. It could mean, in a hospital situation, saving lives, sort of broad definition of monetization. But it was really understanding how data contributed to value, and then finding ways to operationalize that to speed up time to value, to lower cost, to lower risk. And that required a lot of things. It required new skill sets, new training. It required a partnership with the lines of business. It required new technologies like artificial intelligence, which have just only recently come into a point where it's gone mainstream. Of course, when I started in the business several years ago, AI was the hot topic, but you didn't have the compute power. You didn't have the data, you didn't have the cloud. So we see the new innovation engine, not as Moore's Law, the doubling of transistors every 18 months, doubling of performance. Really no, we see the new innovation cocktail as data as the substrate, applying machine intelligence to that data, and then scaling it with the cloud. And through that cloud model, being able to attract startups and innovation. I come back to the chief data officer here, and IBM Chief Data Officer Summit, that's really where the chief data officer comes in. Now, the role in the organization is fuzzy. If you ask people what's a chief data officer, you'll get 20 different answers. Many answers are focused on compliance, particularly in what emerged, again, in those regulated industries: financial service, healthcare, and government. Those are the first to have chief data officers. But now CDOs have gone mainstream. So what we're seeing here from IBM is the broadening of that role and that definition and those responsibilities. Confusing things is the chief digital officer or the chief analytics officer. Those are roles that have also emerged, so there's a lot of overlap and a lot of fuzziness. To whom should the chief data officer report? Many say it should not be the CIO. Many say they should be peers. Many say the CIO's responsibility is similar to the chief data officer, getting value out of data, although I would argue that's never really been the case. The role of the CIO has largely been to make sure that the technology infrastructure works and that applications are delivered with high availability, with great performance, and are able to be developed in an agile manner. That's sort of a more recent sort of phenomenon that's come forth. And the chief digital officer is really around the company's face. What does that company's brand look like? What does that company's go-to-market look like? What does the customer see? Whereas the chief data officer's really been around the data strategy, what the sort of framework should be around compliance and governance, and, again, monetization. Not that they're responsible for the monetization, but they responsible for setting that framework and then communicating it across the company, accelerating the skill sets and the training of existing staff and complementing with new staff and really driving that framework throughout the organization in partnership with the chief digital officer, the chief analytics officer, and the chief information officer. That's how I see it anyway. Martin Schroeder, the senior vice president of IBM, came on today with Inderpal Bhandari, who is the chief data officer of IBM, the global chief data officer. Martin Schroeder used to be the CFO at IBM. He talked a lot, kind of borrowing from Ginni Rometty's themes in previous conferences, chapter one of digital which he called random acts of digital, and chapter two is how to take this mainstream. IBM makes a big deal out of the fact that it doesn't appropriate your data, particularly your personal data, to sell ads. IBM's obviously in the B2B business, so that's IBM's little back-ended shot at Google and Facebook and Amazon who obviously appropriate our data to sell ads or sell goods. IBM doesn't do that. I'm interested in IBM's opinion on big tech. There's a lot of conversations now. Elizabeth Warren wants to break up big tech. IBM was under the watchful eye of the DOJ 25 years ago, 30 years ago. IBM essentially had a monopoly in the business, and the DOJ wanted to make sure that IBM wasn't using that monopoly to hurt consumers and competitors. Now what IBM did, the DOJ ruled that IBM had to separate its applications business, actually couldn't be in the applications business. Another ruling was that they had to publish the interfaces to IBM mainframes so that competitors could actually build plug-compatible products. That was the world back then. It was all about peripherals plugging into mainframes and sort of applications being developed. So the DOJ took away IBM's power. Fast forward 30 years, now we're hearing Google, Amazon, and Facebook coming under fire from politicians. Should they break up those companies? Now those companies are probably the three leaders in AI. IBM might debate that. I think generally, at theCUBE and SiliconANGLE, we believe that those three companies are leading the charge in AI, along with China Inc: Alibaba, Tencent, Baidu, et cetera, and the Chinese government. So here's the question. What would happen if you broke up big tech? I would surmise that if you break up big tech, those little techs that you break up, Amazon Web Services, WhatsApp, Instagram, those little techs would get bigger. Now, however, the government is implying that it wants to break those up because those entities have access to our data. Google's got access to all the search data. If you start splitting them up, that'll make it harder for them to leverage that data. I would argue those small techs would get bigger, number one. Number two, I would argue if you're worried about China, which clearly you're seeing President Trump is worried about China, placing tariffs on China, playing hardball with China, which is not necessarily a bad thing. In fact, I think it's a good thing because China has been accused, and we all know, of taking IP, stealing IP essentially, and really not putting in those IP protections. So, okay, playing hardball to try to get a quid pro quo on IP protections is a good thing. Not good for trade long term. I'd like to see those trade barriers go away, but if it's a negotiation tactic, okay. I can live with it. However, going after the three AI leaders, Amazon, Facebook, and Google, and trying to take them down or break them up, actually, if you're a nationalist, could be a bad thing. Why would you want to handcuff the AI leaders? Third point is unless they're breaking the law. So I think that should be the decision point. Are those three companies, and others, using monopoly power to thwart competition? I would argue that Microsoft actually did use its monopoly power back in the '80s and '90s, in particular in the '90s, when it put Netscape out of business, it put Lotus out of business, it put WordPerfect out of business, it put Novell out of the business. Now, maybe those are strong words, but in fact, Microsoft's bundling, its pricing practices, caught those companies off guard. Remember, Jim Barksdale, the CEO of Netscape, said we don't need the browser. He was wrong. Microsoft killed Netscape by bundling Internet Explorer into its operating system. So the DOJ stepped in, some would argue too late, and put handcuffs on Microsoft so they couldn't use that monopoly power. And I would argue that you saw from that two things. One, granted, Microsoft was overly focused on Windows. That was kind of their raison d'etre, and they missed a lot of other opportunities. But the DOJ definitely slowed them down, and I think appropriately. And if out of that myopic focus on Windows, and to a certain extent, the Department of Justice and the government, the FTC as well, you saw the emergence of internet companies. Now, Microsoft did a major pivot to the internet. They didn't do a major pivot to the cloud until Satya Nadella came in, and now Microsoft is one of those other big tech companies that is under the watchful eye. But I think Microsoft went through that and perhaps learned its lesson. We'll see what happens with Facebook, Google, and Amazon. Facebook, in particular, seems to be conflicted right now. Should we take down a video that has somewhat fake news implications or is a deep hack? Or should we just dial down? We saw this recently with Facebook. They dialed down the promotion. So you almost see Facebook trying to have its cake and eat it too, which personally, I don't think that's the right approach. I think Facebook either has to say damn the torpedoes. It's open content, we're going to promote it. Or do the right thing and take those videos down, those fake news videos. It can't have it both ways. So Facebook seems to be somewhat conflicted. They are probably under the most scrutiny now, as well as Google, who's being accused, anyway, certainly we've seen this in the EU, of promoting its own ads over its competitors' ads. So people are going to be watching that. And, of course, Amazon just having too much power. Having too much power is not necessarily an indication of abusing monopoly power, but you know the government is watching. So that bears watching. theCUBE is going to be covering that. We'll be here all day, covering the IBM CDO event. I'm Dave Vallente, you're watching theCUBE. #IBMCDO, DM us or Tweet us @theCUBE. I'm @Dvallente, keep it right there. We'll be right back right after this short break. (upbeat music)

>> 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.

(electronica) >> 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 Schroeter 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, 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're spending a lot of time with clients. What are they telling you about disruption and digital transformation? >> Yeah, you know the interesting thing Dave, is the first thing every CEO starts with now is that "I run a technology company." And it doesn't matter if they're writing code or manufacturing corrugated cardboard boxes, every CEO believes they are running a technology company. Now interestingly, maybe we could've predicted this already five or six years ago because we run a CEO survey, we run a 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 3-5 years. It led. The CFO lagged, the CMO lagged, everyone else. Like, CEO saw it first. So CEOs now believe they are running technology businesses, and 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, we shorthand of digitizing the enterprise. Or, what does it mean to become a digitally enable enterprise? It really is about how to 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 touchpoint of their journey. >> So that brings up a point. Every CEO I talk to is 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? Is digital businesses 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'll 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 backend 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 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're never done. But feel like you're making some good progress there? Internally at IBM specifically. >> Yes, 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 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 Rometty two IBM thinks ago talk about incumbent disruptors, which resonates, 'cause 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. You know we still lead the world in patents so have the benefit. For our 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 to do today. Helping them take their incumbent advantage of data, having data, helping them co-create. We're working with them to co-create solutions that they can deploy and then helping them to put that into work, into production, if you will, in their environments and in their workflows. >> So one of the things you stressed today, two of the things. You've talked about transparency, and open digital trade. I want to get into the latter, but talk about what's important in Chapter Two. Just, what are those ingredients of success? You've 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 is never giving up its source code to our government, and we'd leave the country first." >> We wouldn't give up our source code. >> So what are some of those success factors that we need to be thinking about in that context? >> If we look at IBM. IBM today runs, you know 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 our 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 you're data?" And importantly, who is someone else going to trust with your data, with your systems? Any bank can maybe figure out, you know, 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 run the hearts and lungs of the enterprise world. >> Yeah, 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 at 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 frow freely and the kinds of data that should absolutely, personal information is not what we're talking about, right? 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. Citizen's data, healthcare data, might have to stay, because they're protecting their citizen's privacy. That's the issue I think, that most governments are on. So we have disaggregate 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 is 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. >> Right. >> Right? So for instance, you could have an encryption or a hashtag. Or hash, sorry. Not a hashtag. A hash, say, off 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, you know, essentially you're able to satisfy the client, the customer. And 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. Martin touched on a bunch of those in the keynote with regard to, you know, 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 in 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. 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 we don't create a series of mandates that force someone to store data here. If you want to be in business in a country, the country shouldn't say "Well if you want to business here "you have to store all your data here." It tends to be done on the auspice of a security concern, but we know enough about security 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 it's 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 intellectual property issue there, right? >> It's 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, and- >> 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 aligned 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 very good, but they're three separate things. We need to bring it all together to have one. >> So it was a good example. GDDPR maybe as a framework that seems to be seeping its way into other areas. >> So GDPR is an important discussion, but that's the privacy discussion wrapped around a broader trade issue. But privacy is important. GDPR does a good job on it, but we have a broader trade issue of data. >> Inderpal give me the final word, it's kind of your show. >> Well, you know. 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. And maybe the way to think about it is certain data you do need controls on. And it's more of the form in which the data flows that you restrict. As opposed to letting the data flow at all. >> What do you mean? >> So the hash example that I gave you. It's okay for the hash to go across, 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 in place so that we are not impeding the progress. That's going to be inevitable when you do have the free flow of data. >> 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, they won't be exposed. >> All right. Inderpal, Martin, I know you got to run. Thanks so much for coming out. >> Thank you. Thanks for the talk. >> Thank you >> You're welcome. All right. Keep it right there everybody, we'll be back with our next guest from IBMCDO Summit in San Francisco. You're watching theCube. (electronica)

>> Live from San Francisco, California, it's the theCUBE, covering the IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back to San Francisco everybody. You're watching theCUBE, 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 Chief Data Officer Summit, 10th anniversary. Seth Dobrin is here, he's the Vice President and Chief Data Officer of the IBM Analytics Group. Seth, always a pleasure to have you on. Good to see you again. >> Yeah, thanks for having me back Dave. >> You're very welcome. So I love these events you get a chance to interact with chief data officers, guys like yourself. We've been talking a lot today about IBM's internal transformation, how IBM itself is operationalizing AI and maybe we can talk about that, but I'm most interested in how you're pointing that at customers. What have you learned from your internal experiences and what are you bringing to customers? >> Yeah, so, you know, I was hired at IBM to lead part of our internal transformation, so I spent a lot of time doing that. >> Right. >> I've also, you know, when I came over to IBM I had just left Monsanto where I led part of their transformation. So I spent the better part of the first year or so at IBM not only focusing on our internal efforts, but helping our clients transform. And out of that I found that many of our clients needed help and guidance on how to do this. And so I started a team we call, The Data Science an AI Elite Team, and really what we do is we sit down with clients, we share not only our experience, but the methodology that we use internally at IBM so leveraging things like design thinking, DevOps, Agile, and how you implement that in the context of data science and AI. >> I've got a question, so Monsanto, obviously completely different business than IBM-- >> Yeah. >> But when we talk about digital transformation and then talk about the difference between a business and a digital business, it comes down to the data. And you've seen a lot of examples where you see companies traversing industries which never used to happen before. You know, Apple getting into music, there are many, many examples, and the theory is, well, it's 'cause it's data. So when you think about your experiences of a completely different industry bringing now the expertise to IBM, were there similarities that you're able to draw upon, or was it a completely different experience? >> No, I think there's tons of similarities which is, which is part of why I was excited about this and I think IBM was excited to have me. >> Because the chances for success were quite high in your mind? >> Yeah, yeah, because the chance for success were quite high, and also, you know, if you think about it there's on the, how you implement, how you execute, the differences are really cultural more than they're anything to do with the business, right? So it's, the whole role of a Chief Data Officer, or Chief Digital Officer, or a Chief Analytics Officer, is to drive fundamental change in the business, right? So it's how do you manage that cultural change, how do you build bridges, how do you make people, how do you make people a little uncomfortable, but at the same time get them excited about how to leverage things like data, and analytics, and AI, to change how they do business. And really this concept of a digital transformation is about moving away from traditional products and services, more towards outcome-based services and not selling things, but selling, as a Service, right? And it's the same whether it's IBM, you know, moving away from fully transactional to Cloud and subscription-based offerings. Or it's a bank reimagining how they interact with their customers, or it's oil and gas company, or it's a company like Monsanto really thinking about how do we provide outcomes. >> But how do you make sure that every, as a Service, is not a snowflake and it can scale so that you can actually, you know, make it a business? >> So underneath the, as a Service, is a few things. One is, data, one is, machine learning and AI, the other is really understanding your customer, right, because truly digital companies do everything through the eyes of their customer and so every company has many, many versions of their customer until they go through an exercise of creating a single version, right, a customer or a Client 360, if you will, and we went through that exercise at IBM. And those are all very consistent things, right? They're all pieces that kind of happen the same way in every company regardless of the industry and then you get into understanding what the desires of your customer are to do business with you differently. >> So you were talking before about the Chief Digital Officer, a Chief Data Officer, Chief Analytics Officer, as a change agent making people feel a little bit uncomfortable, explore that a little bit what's that, asking them questions that intuitively they, they know they need to have the answer to, but they don't through data? What did you mean by that? >> Yeah so here's the conversations that usually happen, right? You go and you talk to you peers in the organization and you start having conversations with them about what decisions are they trying to make, right? And you're the Chief Data Officer, you're responsible for that, and inevitably the conversation goes something like this, and I'm going to paraphrase. Give me the data I need to support my preconceived notions. >> (laughing) Yeah. >> Right? >> Right. >> And that's what they want to (voice covers voice). >> Here's the answer give me the data that-- >> That's right. So I want a Dashboard that helps me support this. And the uncomfortableness comes in a couple of things in that. It's getting them to let go of that and allow the data to provide some inkling of things that they didn't know were going on, that's one piece. The other is, then you start leveraging machine learning, or AI, to actually help start driving some decisions, so limiting the scope from infinity down to two or three things and surfacing those two or three things and telling people in your business your choices are one of these three things, right? That starts to make people feel uncomfortable and really is a challenge for that cultural change getting people used to trusting the machine, or in some instances even, trusting the machine to make the decision for you, or part of the decision for you. >> That's got to be one of the biggest cultural challenges because you've got somebody who's, let's say they run a big business, it's a profitable business, it's the engine of cashflow at the company, and you're saying, well, that's not what the data says. And you're, say okay, here's a future path-- >> Yeah. >> For success, but it's going to be disruptive, there's going to be a change and I can see people not wanting to go there. >> Yeah, and if you look at, to the point about, even businesses that are making the most money, or parts of a business that are making the most money, if you look at what the business journals say you start leveraging data and AI, you get double-digit increases in your productivity, in your, you know, in differentiation from your competitors. That happens inside of businesses too. So the conversation even with the most profitable parts of the business, or highly, contributing the most revenue is really what we could do better, right? You could get better margins on this revenue you're driving, you could, you know, that's the whole point is to get better leveraging data and AI to increase your margins, increase your revenue, all through data and AI. And then things like moving to, as a Service, from single point to transaction, that's a whole different business model and that leads from once every two or three or five years, getting revenue, to you get revenue every month, right? That's highly profitable for companies because you don't have to go in and send your sales force in every time to sell something, they buy something once, and they continue to pay as long as you keep 'em happy. >> But I can see that scaring people because if the incentives don't shift to go from a, you know, pay all up front, right, there's so many parts of the organization that have to align with that in order for that culture to actually occur. So can you give some examples of how you've, I mean obviously you ran through that at IBM, you saw-- >> Yeah. >> I'm sure a lot of that, got a lot of learnings and then took that to clients. Maybe some examples of client successes that you've had, or even not so successes that you've learned from. >> Yeah, so in terms of client success, I think many of our clients are just beginning this journey, certainly the ones I work with are beginning their journey so it's hard for me to say, client X has successfully done this. But I can certainly talk about how we've gone in, and some of the use cases we've done-- >> Great. >> With certain clients to think about how they transformed their business. So maybe the biggest bang for the buck one is in the oil and gas industry. So ExxonMobile was on stage with me at, Think, talking about-- >> Great. >> Some of the work that we've done with them in their upstream business, right? So every time they drop a well it costs them not thousands of dollars, but hundreds of millions of dollars. And in the oil and gas industry you're talking massive data, right, tens or hundreds of petabytes of data that constantly changes. And no one in that industry really had a data platform that could handle this dynamically. And it takes them months to get, to even start to be able to make a decision. So they really want us to help them figure out, well, how do we build a data platform on this massive scale that enables us to be able to make decisions more rapidly? And so the aim was really to cut this down from 90 days to less than a month. And through leveraging some of our tools, as well as some open-source technology, and teaching them new ways of working, we were able to lay down this foundation. Now this is before, we haven't even started thinking about helping them with AI, oil and gas industry has been doing this type of thing for decades, but they really were struggling with this platform. So that's a big success where, at least for the pilot, which was a small subset of their fields, we were able to help them reduce that timeframe by a lot to be able to start making a decision. >> So an example of a decision might be where to drill next? >> That's exactly the decision they're trying to make. >> Because for years, in that industry, it was boop, oh, no oil, boop, oh, no oil. >> Yeah, well. >> And they got more sophisticated, they started to use data, but I think what you're saying is, the time it took for that analysis was quite long. >> So the time it took to even overlay things like seismic data, topography data, what's happened in wells, and core as they've drilled around that, was really protracted just to pull the data together, right? And then once they got the data together there were some really, really smart people looking at it going, well, my experience says here, and it was driven by the data, but it was not driven by an algorithm. >> A little bit of art. >> True, a lot of art, right, and it still is. So now they want some AI, or some machine learning, to help guide those geophysicists to help determine where, based on the data, they should be dropping wells. And these are hundred million and billion dollar decisions they're making so it's really about how do we help them. >> And that's just one example, I mean-- >> Yeah. >> Every industry has it's own use cases, or-- >> Yeah, and so that's on the front end, right, about the data foundation, and then if you go to a company that was really advanced in leveraging analytics, or machine learning, JPMorgan Chase, in their, they have a division, and also they were on stage with me at, Think, that they had, basically everything is driven by a model, so they give traders a series of models and they make decisions. And now they need to monitor those models, those hundreds of models they have for misuse of those models, right? And so they needed to build a series of models to manage, to monitor their models. >> Right. >> And this was a tremendous deep-learning use case and they had just bought a power AI box from us so they wanted to start leveraging GPUs. And we really helped them figure out how do you navigate and what's the difference between building a model leveraging GPUs, compared to CPUs? How do you use it to accelerate the output, and again, this was really a cost-avoidance play because if people misuse these models they can get in a lot of trouble. But they also need to make these decisions very quickly because a trader goes to make a trade they need to make a decision, was this used properly or not before that trade is kicked off and milliseconds make a difference in the stock market so they needed a model. And one of the things about, you know, when you start leveraging GPUs and deep learning is sometimes you need these GPUs to do training and sometimes you need 'em to do training and scoring. And this was a case where you need to also build a pipeline that can leverage the GPUs for scoring as well which is actually quite complicated and not as straight forward as you might think. In near real time, in real time. >> Pretty close to real time. >> You can't get much more real time then those things, potentially to stop a trade before it occurs to protect the firm. >> Yeah. >> Right, or RELug it. >> Yeah, and don't quote, I think this is right, I think they actually don't do trades until it's confirmed and so-- >> Right. >> Or that's the desire as to not (voice covers voice). >> Well, and then now you're in a competitive situation where, you know. >> Yeah, I mean people put these trading floors as close to the stock exchange as they can-- >> Physically. >> Physically to (voice covers voice)-- >> To the speed of light right? >> Right, so every millisecond counts. >> Yeah, read Flash Boys-- >> Right, yeah. >> So, what's the biggest challenge you're finding, both at IBM and in your clients, in terms of operationalizing AI. Is it technology? Is it culture? Is it process? Is it-- >> Yeah, so culture is always hard, but I think as we start getting to really think about integrating AI and data into our operations, right? As you look at what software development did with this whole concept of DevOps, right, and really rapidly iterating, but getting things into a production-ready pipeline, looking at continuous integration, continuous development, what does that mean for data and AI? And these concept of DataOps and AIOps, right? And I think DataOps is very similar to DevOps in that things don't change that rapidly, right? You build your data pipeline, you build your data assets, you integrate them. They may change on the weeks, or months timeframe, but they're not changing on the hours, or days timeframe. As you get into some of these AI models some of them need to be retrained within a day, right, because the data changes, they fall out of parameters, or the parameters are very narrow and you need to keep 'em in there, what does that mean? How do you integrate this for your, into your CI/CD pipeline? How do you know when you need to do regression testing on the whole thing again? Does your data science and AI pipeline even allow for you to integrate into your current CI/CD pipeline? So this is actually an IBM-wide effort that my team is leading to start thinking about, how do we incorporate what we're doing into people's CI/CD pipeline so we can enable AIOps, if you will, or MLOps, and really, really IBM is the only company that's positioned to do that for so many reasons. One is, we're the only one with an end-to-end toolchain. So we do everything from data, feature development, feature engineering, generating models, whether selecting models, whether it's auto AI, or hand coding or visual modeling into things like trust and transparency. And so we're the only one with that entire toolchain. Secondly, we've got IBM research, we've got decades of industry experience, we've got our IBM Services Organization, all of us have been tackling with this with large enterprises so we're uniquely positioned to really be able to tackle this in a very enterprised-grade manner. >> Well, and the leverage that you can get within IBM and for your customers. >> And leveraging our clients, right? >> It's off the charts. >> We have six clients that are our most advanced clients that are working with us on this so it's not just us in a box, it's us with our clients working on this. >> So what are you hoping to have happen today? We're just about to get started with the keynotes. >> Yeah. >> We're going to take a break and then come back after the keynotes and we've got some great guests, but what are you hoping to get out of today? >> Yeah, so I've been with IBM for 2 1/2 years and I, and this is my eighth CEO Summit, so I've been to many more of these than I've been at IBM. And I went to these religiously before I joined IBM really for two reasons. One, there's no sales pitch, right, it's not a trade show. The second is it's the only place where I get the opportunity to listen to my peers and really have open and candid conversations about the challenges they're facing and how they're addressing them and really giving me insights into what other industries are doing and being able to benchmark me and my organization against the leading edge of what's going on in this space. >> I love it and that's why I love coming to these events. It's practitioners talking to practitioners. Seth Dobrin thanks so much for coming to theCUBE. >> Yeah, thanks always, Dave. >> Always a pleasure. All right, keep it right there everybody we'll be right back right after this short break. You're watching, theCUBE, live from San Francisco. Be right back.

>> live from San Francisco, California It's the Q covering the IBM Chief Data Officer Summit brought to you by IBM. >> We're back. You're watching the Cube, the leader in life Tech coverage. My name is Dave Volant Day, and we're covering the IBM Chief Data officer event hashtag IBM CDO is the 10th year that IBM has been running This event on the New Cube has been covering this for the last I'd say four or five years. Beth rottenness here. She's the distinguished engineer and principal data scientist. Cognitive within GTS Large Service's organization within IBM. Bet thanks so much for coming on the Cube. >> Absolutely. Thank you for having me. >> So you're very welcome. So really interesting sort of title. I'm inferring a lot. Um, and you're sexually transforming lives through data and analytics. Talk about your role a little bit. >> So my role is to infuse workforce transformation with cognitive. I typically we go from I think you've heard the ladder to a I. But as we move up that ladder and we can actually >> apply artificial intelligence and NLP, which is a lot of what I'm doing, >> it is it's instrumental in being able to see human beings in a lot more dimensions. So when we classify humans by a particular job role skill set, we often don't know that they have a passion for things like coding or anything else. And so we're really doing a lot more where we're getting deeper and being able to match your supply and demand in house as well as know when we have a demand for someone. And this person almost meets that demand. Based on all the different dimensionality that weaken dio, >> we can >> put them into this specific training class and then allow them to go through that training class so that we can upgrade the entire upscale and reschedule the entire work force. >> So one of the challenges you're working on is trying to operationalize machine intelligence and obviously starts with that training and skill level so well, it's not easy company the size of of I B M E. You're starting the GTS group, which probably has an affinity, at least conceptually, for transformation. That's what you guys do for your clients. So how's that going? You know, where are you in that journey? >> I think that we're in the journey and we're doing really well. I think that a lot of our people and the people who are actually working on the ground, we're talking to our clients every single day. So people on the helped us, they're talking to clients and customers. They understand what that client is doing. They understand the means, the troops, the mores, the language of the customer, of the organization of the customer, in the client, giving those people skills to understand what they can do better. To help solve our client's problems is really what it's all about. So understanding how we can take all of the unstructured data, all of like the opportunity for understanding what skills those people have on the ground and then being able to match that to the problems that our clients and customers are having. So it's a great opportunity. I think, that I've been in GTS my entire career and being an I t. I think that you understand this is where you store or create or, you know, manage all of the data in an entire enterprise organization, being able to enable and empower the people to be ableto upscale and Reese kill themselves so that they can get access to that so that we can do better for our clients and customers. >> So when you think about operations, folks, you got decades of skills that have built up you. D. B A is, you got network engineers, you got storage administrators. You know the VM add men's, you know, Unix. Add men's, I mean and a lot of those jobs. Air transforming clearly people don't want to invest is much in heavy lifting and infrastructure deployment, right? They want to go up the stack, if you will. So my question is, as you identify opportunities for transformation, I presume it's a lot of the existing workforce that you're transforming. You're not like saying, Okay, guys, you're out. What is gonna go retrain or bring in new people? Gonna retrain existing folks? How's that going? What's their appetite for that? Are they eagerly kind of lining up for this? You could describe that dynamic. >> I think the bits on the ground, they're very hungry. Everyone is so, so, so hungry because they understand what's coming on. They listen to the messages, they're ready. We were also in flexing. I'm sure you've heard of the new collar program were influencing a lot of youth as early professional hires. I have 2 16 year olds in the 17 year old on my team as interns from a P Tech program in Boulder, and getting that mix in that diversity is really all what it's about. We need that diversity of thought. We need that understanding of how we can start to do these things and how people can start to reach for new ways to work. >> All right, so I love this top of the cube we've we've covered, you know, diversity, women in tech. But so let's talk about that a little bit. You just made a statement that you need that diversity. Why is it so important other than it's the right thing to do? What's the what's the business effect of bringing diversity to the table? >> I think that would. We're searching for information truth if you want. If you want to go there, you need a wide variance of thought, the white of your variance, the more standard you're me, and it's actually a mathematical theory. Um, so this is This is something that is part of our truth. We know that diversity of thoughts we've been working. I run and sponsor the LGBT Q Plus group. I do women's groups in the B A R G's and then we also are looking at neuro diversity and really understanding what we can bring in as far as like, a highly diverse workforce. Put them all together, give them the skills to succeed. Make sure that they understand that the client is absolutely the first person that they're looking at in the first person that they're using Those skills on enable them to automate, enable them to stop doing those repeatable tasks. And there's so much application of a I that we can now make accessible so that people understand how to do this at every single level. >> So it's a much wider scope of an observation space. You're sort of purposefully organizing. So you eliminate some of that sampling bias and then getting to the truth. As you say, >> I think that in order to come up with ethical and explainable, aye, aye, there's definitely a way to do this. We know how to do it. It's just hard, and I think that a lot of people want to reach for machine learning or neural nets that spit out the feature without really understanding the context of the data. But a piece of data is an artifact of a human behavior, so you have to trace it all the way back. What process? What person who put it there? Why did they put it there? What was that? When we when we look at really simple things and say, Why are all these tickets classified in this one way? It's because when you observe the human operator, they're choosing the very first thing human behaviors put data in places or human behaviors create machines to put data in places. All of this can be understood if we look at it in a little bit of a different way. >> I thought I had was. So IBM is Business is not about selling ads, so there's no one sent to future appropriate our data to sell advertising. However, if we think about IBM as an internal organism, there's certain incentive structures. There's there's budgets, there's resource is, and so there's always incentives to game the system. And so it sounds like you're trying to identify ways in which you can do the right thing right thing for the business right for people and try to take some of those nuances out of the equation. Is that >> so? From an automation perspectively build digital management system. So all the executives can go in a room and not argue about whose numbers are correct, and they can actually get down to the business of doing business. From the bottoms up perspective, we're enabling the workforce to understand how to do that automation and how to have not only the basic tenets of data management but incorporate that into a digital management system with tertiary and secondary and triangulation and correlations so that we have the evidence and we can show data providence for everything that we're doing. And we have this capability today we're enabling it and operational izing. It really involves a cultural transformation, which is where people like me come in. >> So in terms of culture, so incentives drive behavior, how have you thought through and what are you doing in terms of applying new types of incentives? And how's that working? >> So when we start to measure skills were not just looking at hard skills. We're looking at soft skills, people who are good collaborators, people who have grit, people who are good leaders, people who understand how to do things over and over and over again in a successful manner. So when you start measuring your successful people, you start incentivizing the behaviors that you want to see. And when you start measuring people who can collaborate globally in global economies that that is our business as IBM, that is who we want to see. And that's how we're incentivizing the behaviors that we want to. D'oh. >> So when I look at your background here, obviously you're you're a natural fit for this kind of transformation. So you were You have an anthropology background language. Your data scientist, you do modeling. >> I always say I'm a squishy human data scientist, but I got to work with a huge group of people to create the data science profession with an IBM and get that accredited through open group. And that's something we're very passionate about is to give people a career past so that they know where their next step is. And it's all about moving to growth and sustainable growth by making sure that the workforce knows how value they are by IBM and how valuable they are by our clients. What does >> success look like to you? >> I think success is closer than we think. I think that success is when we have everybody understanding everybody, understanding what it's like to pick up the phone and answer a customer service call from our client and customer and be able to empathize and sympathize and fix the problem. We have 350,000 human beings. We know somebody in some circle that can help fix a client's problem. I think success looks like being able to get that information to the right people at the right time and give people a path so that they know that they're on the boat together, all rowing together in order to make our clients successful. >> That's great. I love the story. Thanks so much for coming on the hearing. You're very welcome. Keep it right there, but we'll be back with our next guest is a day. Violante. We're live from Fisherman's. More for the IBM CDO Chief Data officer event. Right back sticker The cube dot net is where the

>> 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 at the IBM CDO conference. You're watching theCUBE, the leader in live tech coverage. My name is Dave Volante, Joe Selle is here. He's the Global Advanced Analytics and Cognitive Lead at IBM, Boston base. Joe, good to see you again. >> You to Dave. >> And Jerry Gupta, the Senior Vice President and Digital Catalyst at Swiss Re Institute at Swiss Re, great to see you. Thanks for coming on. >> Thank you for having me Dave. >> You're very welcome. So Jerry, you've been at this event now a couple of years, we've been here I think the last four or five years and in the early, now this goes back 10 years this event, now 10 years ago, it was kind of before the whole big data meme took off. It was a lot of focus I'm sure on data quality and data compliance and all of a sudden data became the new source of value. And then we rolled into digital transformation. But how from your perspective, how have things changed? Maybe the themes over the last couple of years, how have they changed? >> I think, from a theme perspective, I would frame the question a little bit differently, right? For me, this conference is a must have on my calendar, because it's very relevant. The topics are very current. So two years ago, when I first attended this conference, it was about cyber and when we went out in the market, they were not too many companies talking about cyber. And so you come to a place like this and you're not and you're sort of blown away by the depth of knowledge that IBM has, the statistics that you guys did a great job presenting. And that really helped us inform ourselves about the cyber risk that we're going on in cyber and so evolve a little bit the consistent theme is it's relevant, it's topical. The other thing that's very consistent is that you always learn something new. The struggle with large conferences like this is sometimes it becomes a lot of me too environment. But in conference that IBM organizes the CDO, in particular, I always learn something new because the practitioners, they do a really good job curating the practitioners. >> And Joe, this has always been an intimate event. You do 'em in San Francisco and Boston, it's, a couple hundred people, kind of belly to belly interactions. So that's kind of nice. But how do you scale this globally? >> Well, I would say that is the key question 'cause I think the AI algorithms and the machine learning has been proven to work. And we've infiltrated that into all of the business processes at IBM, and in many of our client companies. But we've been doing proof of concepts and small applications, and maybe there's a dozen or 50 people using it. But the the themes now are around scale AI at scale. How do you do that? Like we have a remit at IBM to get 100,000 IBMers that's the real number. On our Cognitive Enterprise Data Platform by the end of this calendar year, and we're making great progress there. But that's the key question, how do you do that? and it involves cultural issues of teams and business process owners being willing to share the data, which is really key. And it also involves technical issues around cloud computing models, hybrid public and private clouds, multi cloud environments where we know we're not the only game in town. So there's a Microsoft Cloud, there's an IBM Cloud, there's another cloud. And all of those clouds have to be woven together in some sort of a multi-cloud management model. So that's the techie geek part. But the cultural change part is equally as challenging and important and you need both to get to 100,000 users at IBM. >> You know guys what this conversation brings into focus for me is that for decades, we've marched to the cadence of Moore's laws, as the innovation engine for our industry, that feels like just so yesterday. Today, it's like you've got this data bedrock that we built up over the last decade. You've got machine intelligence or AI, that you now can apply to that data. And then for scale, you've got cloud. And there's all kinds of innovation coming in. Does that sort of innovation cocktail or sandwich makes sense in your business? >> So there's the innovation piece of it, which is new and exciting, the shiny, new toy. And that's definitely exciting and we definitely tried that. But from my perspective and the perspective of my company, it's not the shiny, new toy that's attractive, or that really moves the needle for us. It is the underlying risk. So if you have the shiny new toy of an autonomous vehicle, what mayhem is it going to cause?, right? What are the underlying risks that's what we are focused on. And Joe alluded to, to AI and algorithms and stuff. And it clearly is a very, it's starting to become a very big topic globally. Even people are starting to talk about the risks and dangers inherent in algorithms and AI. And for us, that's an opportunity that we need to study more, look into deeply to see if this is something that we can help address and solve. >> So you're looking for blind spots, essentially. And then and one of them is this sort of algorithmic risk. Is that the right way to look at it? I mean, how do you think about risk of algorithms? >> So yeah, so algorithmic risk would be I would call blind spot I think that's really good way of saying it. We look at not just blind spots, so risks that we don't even know about that we are facing. We also look at known risks, right? >> So we are one of the largest reinsurers in the world. And we insure just you name a risk, we reinsure it, right? so your auto risk, your catastrophe risk, you name it, we probably have some exposure to it. The blind spot as you call it are, anytime you create something new, there are pros and cons. The shiny, new toy is the pro. What risks, what damage, what liability can result there in that's the piece that we're starting to look at. >> So you got the potentially Joe these unintended consequences of algorithms. So how do you address that? Is there a way in which you've thought through, some kind of oversight of the algorithms? Maybe you could talk about IBM's point of view there. >> Well we have >> Yeah and that's a fantastic and interesting conversation that Jerry and I are having together on behalf of our organizations. IBM knowing in great detail about how these AI algorithms work and are built and are deployed, Jerry and his organization, knowing the bigger risk picture and how you understand, predict, remediate and protect against the risk so that companies can happily adopt these new technologies and put them everywhere in their business. So the name of the game is really understanding how as we all move towards a digital enterprise with big data streaming in, in every format, so we use AI to modify the data to a train the models and then we set some of the models up as self training. So they're learning on their own. They're enhancing data sets. And once we turn them on, we can go to sleep, so they do their own thing, then what? We need a way to understand how these models are producing results. Are they results that we agree with? Are these self training algorithms making these, like railroad trains going off the track? Or are they still on the track? So we want to monitor understand and remediate, but it's at scale again, my earlier comments. So you might be an organization, you might have 10,000 not models at work. You can't watch those. >> So you're looking at the intersection of risk and machine intelligence and then you're, if I understand it correctly applying AI, what I call machine intelligence to oversee the algorithms, is that correct? >> Well yes and you could think of it as an AI, watching over the other AI. That's really what we have 'cause we're using AI in as we envision what might or might not be the future. It's an AI and it's watching other AI. >> That's kind of mind blowing. Jerry, you mentioned autonomous vehicles before that's obviously a potential disruptor to your business. What can you share about how you guys are thinking about that? I mean, a lot of people are skeptical. Like there's not enough data, every time there's a another accident, they'll point to that. What's your point of view on that? From your corporation standpoint are you guys thinking is near term, mid term, very long term or it's sort of this journey, that there's quasi-autonomous that sort of gets us there. >> So on autonomous vehicles or algorithmic risk? >> On autonomous vehicles. >> So, the journey towards full automation is a series of continuous steps, right? So it's a continuum and to a certain extent, we are in a space now, where even though we may not have full autonomy while we're driving, there is significant feedback and signals that a car provides and acts or not in an automated manner that eventually move us towards full autonomy, right? So for example, the anti-lock braking system. That's a component of that, right? which is it prevents the car from skidding out of control. So if you're asking for a time horizon when it might have happened, yeah, at our previous firm, we had done some analysis and the horizons were as sort of aggressive as 15 years to as conservative as 50 years. But the component that we all agreed to where there was not such a wide range was that the cars are becoming more sophisticated because the cars are not just cars, any automobile or truck vehicles, they're becoming more automated. Where does risk lie at each piece? Or each piece of the value chain, right? And the answer is different. If you look at commercial versus personal. If you look at commercial space, autonomous fleets are already on the road. >> Right >> Right? And so the question then becomes where does liability lie? Owner, manufacturer, driver >> Shared model >> Shared, manual versus automated mode, conditions of driving, what decisions algorithm is making, which is when you know, the physics don't allow you to avoid an accident? Who do you end up hitting? (crosstalk) >> Again, not just the technology problem. Now, last thing is you guys are doing a panel, on wowing customers making customers the king, I think, is what the title of it is. What's that all about? And get into that a little bit? >> Sure. Well, we focus as IBM mostly on a B2B framework. So the example that I that I'll share to you is, somewhere between like making a customer or making a client the king, the example is that we're using some of our AI to create an alert system that we call Operations Risks Insights. And so the example that I wanted to share was that, we've been giving this away to nonprofit relief agencies who can deploy it around a geo-fenced area like say, North Carolina and South Carolina. And if you're a relief agency providing flood relief or services to people affected by floods, you can use our solution to understand the magnitude and the potential damage impact from a storm. We can layer up a map with not only normal geospatial information, but socio-economic data. So I can say find the relief agency and I've got a huge storm coming in and I can't cover the entire two-state area. I can say okay, well show me the area where there's greater population density than 1000 per square kilometer and the socio-economic level is, lower than a certain point and those are the people that don't have a lot of resources can't move, are going to shelter in place. So I want to know that because they need my help. >> That's where the risk is. Yeah, right they can't get out >> And we use AI to do to use that those are happy customers, and I've delivered wow to them. >> That's pretty wow, that's right. Jerry, anything you would add to that sort of wow customer experience? Yeah, absolutely, So we are a B2B company as well. >> Yeah. >> And so the span of interaction is dictated by that piece of our business. And so we tried to create wow, by either making our customers' life easier, providing tools and technologies that make them do their jobs better, cheaper, faster, more efficiently, or by helping create, goal create, modify products, such that, it accomplishes the former, right? So, Joe mentioned about the product that you launched. So we have what we call parametric insurance and we are one of the pioneers in the field. And so we've launched three products in that area. For earthquake, for hurricanes and for flight delay. And so, for example, our flight delay product is really unique in the market, where we are able to insure a traveler for flight delays. And then if there is a flight delay event that exceeds a pre established threshold, the customer gets paid without even having to file a claim. >> I love that product, I want to learn more about that. You can say (mumbles) but then it's like then it's not a wow experience for the customer, nobody's happy. So that's for Jerry. Guys, we're out of time. We're going to leave it there but Jerry, Joe, thanks so much for. >> We could go on Dave but thank you Let's do that down the road. Maybe have you guys in Boston in the fall? it'll be great. Thanks again for coming on. >> Thanks Dave. >> All right, keep it right there everybody. We'll back with our next guest. You're watching theCUBE live from IBM CDO in San Francisco. We'll be right back. (upbeat music)

>> Live from San Francisco, California, it's theCUBE, covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> We're back at San Francisco. We're here at Fisherman's Wharf covering the IBM Chief Data Officer event #IBMCDO. This is the tenth year of this event. They tend to bookend them both in San Francisco and in Boston, and you're watching theCUBE, the leader in live tech coverage. My name is Dave Valante. John Thomas is here, Cube alum and distinguished engineer, Director of Analytics at IBM, and somebody who provides technical direction to the data science elite team. John, good to see you again. Steve Aliouk is back. He is the Vice President of Deep Learning in the Global Chief Data Office, thanks for comin' on again. >> No problem. >> Let's get into it. So John, you and I have talked over the years at this event. What's new these days, what are you working on? >> So Dave, still working with clients on implementing data science and AI data use cases, mostly enterprise clients, and seeing a variety of different things developing in that space. Things have moved into broader discussions around AI and how to actually get value out of that. >> Okay, so I know one of the things that you've talked about is operationalizing machine intelligence and AI and cognitive and that's always a challenge, right. Sounds good, we see this potential but unless you change the operating model, you're not going to get the type of business value, so how do you operationalize AI? >> Yeah, this is a good question Dave. So, enterprises, many of them, are beginning to realize that it is not enough to focus on just the coding and development of the models, right. So they can hire super-talented Python TensorFlow programmers and get the model building done, but there's no value in it until these models actually are operationalized in the context of the business. So one aspect of this is, actually we know, we are thinking of this in a very systematic way and talking about this in a prescriptive way. So, you've got to scope your use cases out. You got to understand what is involved in implementing the use case. Then the steps are build, run, manage, and each of these have technical aspects and business aspects around, right. So most people jump right into the build aspect, which is writing the code. Yeah, that's great, but once you build the code, build the models by writing code, how do you actually deploy these models? Whether that is for online invocation or back storing or whatever, how do you manage the performance of these models over time, how do you retrain these models, and most importantly, when these models are in production, how do I actually understand the business metrics around them? 'Cause this goes back to that first step of scoping. What are the business KPI's that the line of business cares about? The data scientist talks about data science metrics, position and recall and Area Under the ROC Curve and accuracy and so on. But how do these relate to business KPI's. >> All right, so we're going to get into each of those steps in a moment, but Steve I want to ask you, so part of your charter, Inderpal, Global Chief Data Officer, you guys have to do this for IBM, right, drink your own champagne, dog footing, whatever you call it. But there's real business reasons for you to do that. So how is IBM operationalizing AI? What kind of learnings can you share? >> Well, the beauty is I got a wide portfolio of products that I can pull from, so that's nice. Like things like AI open to Watson, some of the hardware components, all that stuffs kind of being baked in. But part of the reason that John and I want to do this interview together, is because what he's producing, what his thoughts are kind of resonates very well for our own practices internally. We've got so many enterprise use cases, how are we deciding, you know, which ones to work on, which ones have the data, potentially which ones have the biggest business impact, all those KPI's etcetera, also, in addition to, for the practitioners, once we decide on a specific enterprise use case to work on, when have they reached the level where the enterprise is having a return on investment? They don't need to keep refining and refining and refining, or maybe they do, but they don't know these practitioners. So we have to clearly justify it, and scope it accordingly, or these practitioners are left in this kind of limbo, where they're producing things, but not able to iterate effectively for the business, right? So that process is a big problem I'm facing internally. We got hundreds of internal use cases, and we're trying to iterate through them. There's an immense amount of scoping, understanding, etcetera, but at the same time, we're building more and more technical debt, as the process evolves, being able to move from project to project, my team is ballooning, we can't do this, we can't keep growing, they're not going to give me another hundred head count, another hundred head count, so we're definitely need to manage it more appropriately. And that's where this mentality comes in there's-- >> All right, so I got a lot of questions. I want to start unpacking this stuff. So the scope piece, that's we're setting goals, identifying the metrics, success metrics, KPI's, and the like, okay, reasonable starting point. But then you go into this, I think you call it, the explore or understanding phase. What's that all about, is that where governance comes in? >> That's exactly where governance comes in. Right, so because it is, you know, we all know the expression, garbage in, garbage out, if you don't know what data you're working with for your machine learning and deep learning enterprise projects, you will not have the resource that you want. And you might think this is obvious, but in an enterprise setting, understanding where the data comes from, who owns the data, who work on the data, the lineage of that data, who is allowed access to the data, policies and rules around that, it's all important. Because without all of these things in place, the models will be questioned later on, and the value of the models will not realized, right? So that part of exploration or understanding, whatever you want to call it, is about understanding data that has to be used by the ML process, but then at a point in time, the models themselves need to be cataloged, need to be published, because the business as a whole needs to understand what models have been produced out of this data. So who built these models? Just as you have lineage of data, you need lineage of models. You need to understand what API's are associated with the models that are being produced. What are the business KPI's that are linked to model metrics? So all of that is part of this understand and explore path. >> Okay, and then you go to build. I think people understand that, everybody wants to start there, just start the dessert, and then you get into the sort of run and manage piece. Run, you want a time to value, and then when you get to the management phase, you really want to be efficient, cost-effective, and then iterative. Okay, so here's the hard question here is. What you just described, some of the folks, particularly the builders are going to say, "Aw, such a waterfall approach. Just start coding." Remember 15 years ago, it was like, "Okay, how do we "write better software, just start building! "Forget about the requirements, "Just start writing code." Okay, but then what happens, is you have to bolt on governance and security and everything else so, talk about how you are able to maintain agility in this model. >> Yeah, I was going to use the word agile, right? So even in each of these phases, it is an agile approach. So the mindset is about agile sprints and our two week long sprints, with very specific metrics at the end of each sprint that is validated against the line of business requirements. So although it might sound waterfall, you're actually taking an agile approach to each of these steps. And if you are going through this, you have also the option to course correct as it goes along, because think of this, the first step was scoping. The line of business gave you a bunch of business metrics or business KPI's they care about, but somewhere in the build phase, past sprint one or sprint 2, you realize, oh well, you know what, that business KPI is not directly achievable or it needs to be refined or tweaked. And there is that circle back with the line of business and a course correction as it was. So it's a very agile approach that you have to take. >> Are they, are they, That's I think right on, because again, if you go and bolt on compliance and governance and security after the fact, we know from years of experience, that it really doesn't work well. You build up technical debt faster. But are these quasi-parallel? I mean there's somethings that you can do in build as the scoping is going on. Is there collaboration so you can describe, can you describe that a little bit? >> Absolutely, so for example, if I know the domain of the problem, I can actually get started with templates that help me accelerate the build process. So I think in your group, for example, IBM internally, there are many, many templates these guys are using. Want to talk a little bit about that? >> Well, we can't just start building up every single time. You know, that's again, I'm going to use this word and really resonate it, you know it's not extensible. Each project, we have to get to the point of using templates, so we had to look at those initiatives and invest in those initiatives, 'cause initially it's harder. But at least once we have some of those cookie-cutter templates and some of them, they might have to have abstractions around certain parts of them, but that's the only way we're ever able to kind of tackle so many problems. So no, without a doubt, it's an important consideration, but at the same time, you have to appreciate there's a lot of projects that are fundamentally different. And that's when you have to have very senior people kind of looking at how to abstract those templates to make them reusable and consumable by others. >> But the team structure, it's not a single amoeba going through all these steps right? These are smaller teams that are, and then there's some threading between each step? >> This is important. >> Yeah, that's tough. We were just talking about that concept. >> Just talking about skills and >> The bind between those groups is something that we're trying to figure out how to break down. 'Cause that's something he recognizes, I recognize internally, but understanding that those peoples tasks, they're never going to be able to iterate through different enterprise problems, unless they break down those borders and really invest in the communication and building those tools. >> Exactly, you talk about full stack teams. So you, it is not enough to have coding skills obviously. >> Right. What is the skill needed to get this into a run environment, right? What is the skill needed to take metrics like not metrics, but explainability, fairness in the moderates, and map that to business metrics. That's a very different skill from Python coding skills. So full stack teams are important, and at the beginning of this process where someone, line of business throws 100 different ideas at you, and you have to go through the scoping exercise, that is a very specific skill that is needed, working together with your coders and runtime administrators. Because how do you define the business KPI's and how do you refine them later on in the life cycle? And how do you translate between line of business lingo and what the coders are going to call it? So it's a full stack team concept. It may not necessarily all be in one group, it may be, but they have to work together across these different side loads to make it successful. >> All right guys, we got to leave it there, the trains are backing up here at IBM CDO conference. Thanks so much for sharing the perspectives on this. All right, keep it right there everybody. You're watchin' "theCUBE" from San Francisco, we're here at Fisherman's Wharf. The IBM Chief Data Officer event. Right back. (bubbly electronic music)

>> Live from Boston, it's theCUBE. Covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back everyone to theCUBE's live coverage of the IBM Chief Data Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight along with my co-host Paul Gillin. We're joined by Ash Dhupar, he is the Chief Analytics Officer at Publishers Clearing House. Thank you so much for coming on theCUBE. >> Thank you Rebecca for calling me here. >> So Publishers Clearing House is a billion-dollar company. We think of it as the sweepstakes company, we think of the giant checks and be the Prize Patrol surprising contestants, but it's a whole lot more than that. Tell our viewers a little bit, just explain all the vast amount of businesses that you're in. >> Sure, so, in a nutshell, we are a media and entertainment company with a large base of customers, about 100 million customers who are motivated with the chance to win. That's the sweepstakes angle to it. And we have, you can categorize the business into two buckets. One is our media and entertainment side, which is the publishing side. And then the other is our retail side which is where we sell merchandise to our customers. Think of us as a catalog and an e-commerce company. On the media and entertainment side, we have a very good engagement with our customers, we get about two billion page views on a monthly basis on our website. We, about 15 million unique customers on a monthly basis are coming to the site and they spend a considerable amount of time with us on an average, anywhere between 12 to 15 minutes, depending on, you know the type of the customers. Some of our very heavily-engaged customers can spend as much as about two hours a day with us. (Rebecca and John laughs) >> Trying to win that, that either the big prize or there are small prizes like, if you go on our site, there's a winner everyday, like there could be 1,000 dollar winner everyday playing a certain type of a game. So that's the media and the entertainment side of our business, that's completely ad-supported. And then we are the retail side of the business is we are in direct mail, so the traditional, we would send someone a direct mail package. And an e-commerce company as well. Just as a small nugget of information, we are. We send almost about 400 million pieces of physical mail which is including our packages that are sent and so on and so forth and though also still a large direct mail company. Still profitable and still growing. >> I'm sure the US Postal Service is grateful for your support. (laughs) They need all the help they can get. You collect, essentially, the prize money, is your cost of data acquisition and you have a huge database you told us earlier before we started filming of about 100 million people, that you have data on just in the US alone. Now what are you doing at the upper limits of what you're able to do with this data. How are you using this strategically other than just you know personalized email? >> Sure, so I think using data is a core asset for us. We are utilizing in giving our customers better experiences by utilizing the data we have on them. Marrying it with other data sources as well. So that we can personalize the experience. So that we can make your experience when you come on the site better. Or if we are sending something to you in mail, we give you products that are relevant to you. So to bring it down to a little more tactical level, in case of when you are on our site, then on our e-commerce site, there's a product recommendation engine, right? Which goes in and recommends products to you on what products to buy. Those product recommendation engines drive a significant amount of sales, almost about 40% of our sales are driven by the prior recommendation engines that is all understanding of the customer, what you're buying, what you're likely to buy and the algorithms behind it are built with that. >> Can you give another example though, of how, if I were, I mean you said all these customers are united by a common desire to win and to play a game and to win. >> Right. >> But what are some other ways beyond product recommendation engines, which are now sort of old hat. >> Right. >> What other ways are you enhancing the customers experience and personalizing it? >> Sure, sure. So, I'll give you a recent example of where we are utilizing some of the data to give a more relevant experience to the customer. So when a customer comes on our website, right when you're coming to register with us. So, as you register, as you fill in the form, after you give your name, address and your email address and you hit submit, at that very second, there are some algorithms that are running behind the scenes to understand how are you likely to engage with us. How are you going to, let's say, because we have a diverse business, are you likely to buy something from us? Or are you not likely to buy something from us? And if you're not likely to buy something from us, which means I can get you to, and you know not waste your time in showing you merchandise, but I can give you an experience of free-to-play games and you can, within free-to-play games, what type of games like understanding the persona of the person. We could say, hey, you probably are a lotto player or you are a word game puzzle player and we could give you and direct you to those experiences that are more relevant to you. In case of, if you're going to buy something from us, are you likely to buy, you know highly likely to buy or less likely to buy. Depending on that, should I show you just 10 or 15 products or should I show you like more than that? Are you more likely to buy a magazine? So making it more relevant for the customer experience is where it is all about. We use a lot of this data to, to make that happen. >> So analytics is really core to your business. It's the, completely strategic. Where do you sit in the organization, organizational layout, how is that reflected in the way your job is integrated into the organization? >> Sure, so, it is, I'm part of the C-Suite. And I think our CEO, he had this vision, thing he started. He loves data first of all. (laughs) >> Lucky for you. (laughs) >> Thank you. And he truly believes that data and analytics can drive growth and bring innovation from different areas if we utilize it in the best possible way. So A, I am part of that team. And work very closely with each of the business owners. That's the key, out here is like you know, it is, analytics is not in one corner but in the center of all the, all the business areas giving them either insights or building algorithms for them so that we can make either better decisions or we can power growth, depending on which way we are looking at it. >> You're the Chief Analytics Officer and we're here at the Chief Data Summit here, of here. How different are the roles in your mind and do they work together? I mean you have a CTO that is responsible for sort of Chief Data Officer. >> Yes. >> Responsibilities. How do you two collaborate and work together? >> It is a very tight collaboration. And they're two separate jobs but it is a very tight collaboration, we work hand in hand with each other. And the best part I would say is that you know, we're all focused and we're all driving towards how can we drive growth? That's the bottom line, that is where the bucks stops for all of us in the companies. Are we building projects? Are we doing things that is going to grow the company or not? So the collaboration with the CTO is A, a critical piece. They own the infrastructure, as well as the data and when you own the data, which is, in a way, is slightly, I would say, data governance I would say is a thankless job (laughs) believe it or not. But it is a critical job. It is if your data is not right, it is not going to work for whatever you're trying to do, it's the garbage in garbage out, we all know about that. And we work very closely. If there are CAPEX proposals that needs to be put in place because we're going after a certain big project, whether it's putting things together in one place or a 360 view of the customer. All of that is worked hand in hand. We work together in working towards that. >> What is your big data infrastructure like? Is it on the Cloud? Is it your own? Are you Adobe based? What do you use? >> All of the above. >> Oh. (laughter) No, so, what we have is because we are such an old company, you know we still have our legacy Db2 infrastructure. A lot of our backend databases, lot of our backend processes are all attached to that. We have a warehouse, a sequel server warehouse. We also, for our web analytics, we use Google's BigQuery. That's where you collect a lot of data on a daily basis. And recently, I think about three years ago, we went into the Cloud environment. We have a map, our cluster, which was cloud-based and now, we have brought in on prem very recently. >> Back from the Cloud. >> Back from the Cloud, on prem. And there was very good reasoning why we did that. I think frankly, it's cheaper on a longer term to bring that on prem and you are a lot more in control with all the issues with data privacy. So it is. >> Which, I hope you don't mind my interrupting but we have to wrap here and I need to get that question in. (laughs) >> Yes. >> You have data on 100 million consumers. What are you doing with all of the attention being paid for privacy right now? What are you doing to ensure the. >> We have a very, very I would say integrated infrastructure, data governance, data. There's a whole slew of, I would say, people and process around that to make sure that our date is not exposed. Now luckily, it's it's not like PII to the level that it's a health care data. So you are not really, you have information that is crazy but you still have the PII, the name and address of these customers. And as an example, none of the PII data is actually available to even to the analytics folks. It's all stripped, the PII's stripped off. You give us an ID to the customer and frankly the analytics team don't need the PII information to build any algorithms as well. So there is a whole process around keeping the data secure. >> Great, well Ash, thank you so much for coming on theCUBE, it was a pleasure having you. >> Thank you and thank you for inviting me. >> I'm Rebecca Knight for Paul Gillin. We will have more from IBM CDO Summit just after this. (techno music)

(upbeat music) >> Live from Boston. It's theCUBE. Covering IBM Chief Data Officer's Summit. Brought to you by IBM. >> Welcome back to theCUBE's live coverage of IBM's CDO here in beautiful Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Paul Gillen. We're joined by Beth Rudden. She is distinguished engineer, analytics at IBM. Thanks so much for coming on theCUBE, Beth. >> Thank you. >> So your background, you have a master's in anthropology and in classics and Greek. You're a former archeologist. And now here you are, distinguished engineer. There's only 672 in all of IBM. How did you, what are you doing here? (laughs) >> I think that I love data. I love that data represents human behavior and I think that understanding that puzzle and being able to tell that story is something that we need to do more of. And we need to understand how all of the data fits together and how all of the information is created and all the wisdom is created. And that takes a lot of effort from a lot of people and it involves storytelling. I think that 75,000 years of human history, we are always understanding conflict and resolution through storytelling. And I think that if we can have evidence for that using our data, and looking at our data in the business world, as it reflects our strategy, instead of force-fitting our data into our strategy. So I think that that's part of the change that we need to look at it, and I think with the, I would say, second or third hype curve of AI and what we're doing with AI and cognitive today, it really is being able to bind philosophy and psychology and look at it from a computer science perspective. And that's new. >> That's interesting. And we were just speaking with Inderpal earlier about the background that he sees CDOs coming from, and he talked very little about technology. It was all about human so-called soft skills. Are you finding that the CDO role is evolving in a less technical direction over time? >> Absolutely. And I think that, you know, when you're starting to look for outcomes, and as outcomes as they relate to our business, and as they relate to our clients and our customers, we have to be able to have a very diverse and inclusive viewpoint. And we have to be incredibly transparent. I think that is something that we are continuing to do within IBM, where we are really looking at how do we differentiate ourselves based on our expertise and based on our human capital. >> So it's differentiating yourself as an employer of choice. >> Absolutely. >> And attracting, recruiting or training the talent. >> 100%, yes. >> But then also being the expert that your clients come to. So yeah, just... >> So IBM has, you know, we have career frameworks. We have career paths. I was part of a team that created the data science profession at IBM and one of the things that we're looking at, as a differentiating feature, is that we really want people to continuously learn, continuously adapt, develop themselves, develop their skills, because that is our differentiating feature. And I think that, you know, when our clients meet our people they love our people. And this is such an amazing company to be a part of. We have a long history. 107 year history of being one of the most diverse and inclusive companies. 1899, we hired the first black and female person, and in 1953 we had equal opportunity rights 10 years before the Civil Rights Act. So I think that all of these things, you know, lead up to a company that shows that we can adapt and transform. And being an acting CDO for the largest IT system in IBM right now, we are doing amazing things because we are really investing in our people. We are investing in giving them that guidance, that career track. And allowing people to be themselves. Their true selves. >> You're speaking of Global Technology Services Operation, which is currently undergoing a transformation. >> That's right. >> What does the outcome look like? How do you envision the end point? >> I think that I envision the end point, we are in the process of developing our IBM Services Academy and it is a continuous learning platform for Bands Two through general managers. And, you know, one of the stories I like to tell is my general manager who I'm working for on this. You know, she believes so strongly in making sure that everybody has access to all of the available education and everybody is using that type of education and we are looking at transforming how we are measuring what we are doing to incentivize the behaviors that we want to see. And the behaviors that we are looking for are people who are helping other people, and making sure that we are continually being the premier leader of the intellectual brainchild of what we are doing for keeping us in the AI game, in the cognitive game, and making sure that we are understanding every single aspect of that as it relates to our transformation. >> So you're actually tracking and measuring how colleagues collaborate with each other. >> 100%. >> How do you do that? >> You look for words like we and team and you look for people who are enabling other people. And that's something that we can see in the data. Data are artifacts of human behavior. We can see that in our data. We are looking at unstructured data, we are looking at structured data. We are taking this in and we're taking it to what I would call a new level, so that we can see what we are doing, who our people are, and we are able to look at how many of the people are enabling other people and empowering other people. And sometimes this is called the glue, or glue work. I think that there is even a baseball reference for like a glue man. I think that we need to champion people who are enabling and empowering everybody to succeed. >> And are those typically the unsung heroes, would you say? >> Yes. 100%. And I want to sing the hero's song for those unsung heroes. And I want to make sure that those people are noticed and recognized, but I also want to make sure that people know that IBM is this amazing company with a very long history of making sure that we are singing the unsung hero's song. >> But how do you measure the outcome of that? There's got to be a big business bottom line benefit. What does that look like? >> Absolutely. I think that it always starts with our clients. Everything that we do starts with our clients. And in GTS, we have people, we have five to seven year relationships with our clients and customers. These are deep relationships, and they interact with our humans every single day. And we are the men and women who, you know, design and create and run and manage the foundational systems of the world. And every single person, like you cannot book an airline, you cannot pay your bill, you cannot do that, anything, without touching somebody in IBM. We are investing in those people, because those are who is interfacing with our clients and customers, and that is the most important thing to us right now. >> One of the things we were talking about earlier is bringing more women and underrepresented minorities and men into IBM, and into other industries too. So how, we know the technology industry has a very bad reputation, deservedly so, for being a bro culture. How are you personally combating it, and then how do you do it from an institutional perspective? >> Yeah. We have so many programs that are really looking at how we can take and champion diversity. I was very honored to walk into the Best of IBM with my husband a couple years ago, and he looks around and he goes, this is like a UN convention. He's like, you guys are so global. You have so much diversity. And you know, that viewpoint is something that, it's why I work for IBM. It's why I love IBM. I have the ability to understand different cultures, I have the ability to travel around the world. We have, you can work day and night. (laughs) You know, you can talk to India in morning and Australia in the afternoon. It is just, to me, you know, IBM operates in 172 different countries. We have the global infrastructure to be able to handle the type of global teams that we are building. >> When you look at the skills that will be needed in the future as organizations, big data becomes infused into the organization, how will the skill needs change? >> To me, I think that the skill needs are always going to continuously transform. We're always going to get new technology. Most of my data scientists, you know, I really push Python, I really push R, but I think that it's the will more than the skill. I think that it is how people have the attitude and how people collaborate. And that is more important, I think, than some of the skills. And a lot of people, you know, when they are performing data science or performing data engineering, they need to believe that they are doing something that is going to succeed. And that is will. And that's what we, we have seen a huge surge in oral and written communications which is not a hard skill. It's a soft skill. But to me, there's nothing soft about those skills. It takes courage and we have built resiliency because we have had the courage to really enable and empower people to get those types of skills. And that's a lot of where our education is going. >> So that's really an interesting point here. So are you hiring a self-selected group of people, or are you bringing in super smart people who maybe are not as skilled in those areas and bringing them into the culture. I mean, what's coming first here? >> Yeah. I think that we are, our culture is strong. IBM's brand is strong. Our culture is strong. We are investing in the people that we have. And we are investing in, you know, our humans in order to make sure that the people who already have that culture have the skills that they need in order to learn. And that understanding of going from disequilibrium to equilibrium to disequilibrium to learn, that's what we want to teach, so that any of the new technology, any of the new skills, or any of the new platforms that we need to learn, it's something that's inherent with people being able to learn how to learn. >> Beth, thank you so much for coming on theCUBE. >> Yes. >> It was great to have you. >> Thank you. >> I'm Rebecca Knight, for Paul Gillen, we will have more of theCUBE's live coverage of IBM's CDO coming up in just a little bit. (light music)

(light music) >> Live from Boston. It's theCUBE. Covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back everyone, to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host, Rebecca Night. And I'm joined by my co-host, Paul Gillen. We have two guests for this segment. We have Steven Eliuk, who is the Vice President of Deep Learning Global Chief Data Officer at IBM. And Christopher Bannocks, Group Chief Data Officer at IMG. Thanks so much for coming on theCUBE. >> My pleasure. >> Before we get started, Steve, I know you have some very important CUBE fans that you need-- >> I do. >> To give a shout out to. Please. >> For sure. So I missed them on the last three runs of CUBE, so I'd like to just shout out to Santiago, my son. Five years old. And the shortest one, which is Elana. Miss you guys tons and now you're on the air. (all laughing) >> Excellent. To get that important piece of business out. >> Absolutely. >> So, let's talk about Metadata. What's the problem with Metadata? >> The one problem, or the many (chuckles)? >> (laughing) There are a multitude of problems. >> How long ya got? The problem is, it's everywhere. And there's lots of it. And bringing context to that and understanding it from enterprise-wide perspective is a huge challenge. Just connecting to it finding it, or collecting centrally and then understanding the context and what it means. So, the standardization of it or the lack of standardization of it across the board. >> Yeah, it's incredibly challenging. Just the immense scale of metadata at the same time dealing with metadata as Chris mentioned. Just coming up with your own company's glossary of terms to describe your own data. It's kind of step one in the journey of making your data discoverable and governed. Alright, so it's challenging and it's not well understood and I think we're very early on in these stages of describing our data. >> Yeah. >> But we're getting there. Slowly but surely. >> And perhaps in that context it's not only the fact that it's everywhere but actually we've not created structural solutions in a consistent way across industries to be able to structure it and manage it in an appropriate way. >> So, help people do it better. What are some of the best practices for creating, managing metadata? >> Well you can look at diff, I mean, it's such a broad space you can look at different ones. Let's just take the work we do around describing our data and we do that for for the purposes of regulation. For the purposes of GDPR et cetera et cetera. It's really about discovering and providing context to the data that we have in the organization today. So, in that respect it's creating a catalog and making sure that we have the descriptions and the structures of the data that we manage and use in the organization and to give you perhaps a practical example when you have a data quality problem you need to know how to fix it. So, you store, so you create and structure metadata around well, where does it come from, first of all. So what's the journey it's taken to get to the point where you've identified that there's a problem. But also then, who do we go to to fix it? Where did it go wrong in the chain? And who's responsible for it? Those are very simple examples of the metadata around, the transformations the data might have come through to get to its heading point. The quality metrics associated with it. And then, the owner or the data steward that it has to be routed back to to get fixed. >> Now all of those are metadata elements >> All of those, yeah. >> Right? >> 'Cause we're not really talking about the data. The data might be a debit or a credit. Something very simple like that in banking terms. But actually it's got lots of other attributes associated with it which essentially describe that data. So, what is it? Who owns it? What are the data quality metrics? How do I know whether what it's quality is? >> So where do organizations make mistakes? Do they create too much metadata? Do they create poor, is it poorly labeled? Is it not federated? >> Yes. (all laughing) >> I think it's a mix of all of them. One of the things that you know Chris alluded to and you might of understood is that it's incredibly labor-intensive task. There's a lot of people involved. And when you get a lot of people involved in sadly a quite time-consuming, slightly boring job there's errors and there's problem. And that's data quality, that's GDPR, that's government owned entities, regulatory issues. Likewise, if you can't discover the data 'cause it's labeled wrong, that's potential insight that you've now lost. Because that data's not discoverable to a potential project that's looking for similar types of data. Alright, so, kind of step one is trying to scribe your metadata to the organization. Creating a taxonomy of metadata. And getting everybody on board to label that data whether it be short and long descriptions, having good tools et cetera. >> I mean look, the simple thing is... we struggle as... As a capability in any organization we struggle with these terms, right? Metadata, well ya know, if you're talking to the business they have no idea what you're talking about. You've already confused them the minute you mentioned meta. >> Hashtag. >> Yeah (laughs) >> It's a hashtag. >> That's basically what it is. >> Essentially what it is it's just data about data. It's the descriptive components that tell you what it is you're dealing with. If you just take a simple example from finance; An interest rate on it's own tells you nothing. It could be the interest rate on a savings account. It can the interest rate on a bond. But on its own you have no clue, what you're talking about. A maturity date, or a date in general. You have to provide the context. And that is it's relationships to other data and the contexts that it's in. But also the description of what it is you're looking at. And if that comes from two different systems in an organization, let's say one in Spain and one in France and you just receive a date. You don't know what you're looking at. You have not context of what you're looking at. And simply you have to have that context. So, you have to be able to label it there and then map it to a generic standard that you implement across the organization in order to create that control that you need in order to govern your data. >> Are there standards? I'm sorry Rebecca. >> Yes. >> Are there standards efforts underway industry standard why difference? >> There are open metadata standards that are underway and gaining great deal of traction. There are an internally use that you have to standardize anyway. Irrespective of what's happening across the industry. You don't have the time to wait for external standards to exist in order to make sure you standardize internally. >> Another difficult point is it can be region or country specific. >> Yeah. >> Right, so, it makes it incredibly challenging 'cause every region you might work in you might have to have a own sub-glossary of terms for that specific region. And you might have to control the export of certain data with certain terms between regions and between countries. It gets very very challenging. >> Yeah. And then somehow you have to connect to it all to be able to see what it all is because the usefulness of this is if one system calls exactly the same, maps to let's say date. And it's local definition of that is maturity date. Whereas someone else's map date to birthdate you know you've got a problem. You just know you've got a problem. And exposing the problem is part of the process. Understanding hey that mapping's wrong guys. >> So, where do you begin? If your mission is to transform your organization to be one that is data-centric and the business side is sort of eyes glazing over at the mention of metadata. What kind of communication needs to happen? What kind of teamwork, collaboration? >> So, I mean teamwork and collaboration are absolutely key. The communication takes time. Don't expect one blast of communication to solve the problem. It is going to take education and working with people to actually get 'em to realize the importance of things. And to do that you need to start something. Just the communication of the theory doesn't work. No one can ever connect to it. You have to have people who are working on the data for a reason that is business critical. And you need have them experience the problem to recognize that metadata is important. Until they experience the problem you don't get the right amount of traction. So you have to start small and grow. >> And you can use potentially the whip as well. Governance, the regulatory requirements that's a nice one to push things along. That's often helpful. >> It's helpful, but not necessarily popular. >> No, no. >> So you have to give-- >> Balance. >> We're always struggling with that balance. There's a lot of regulation that drives the need for this. But equally, that same regulation essentially drives all of the same needs that you need for analytics. For good measurement of the data. For growth of customers. For delivering better services to customers. All of these things are important. Just the web click information you have that's all essentially metadata. The way we interact with our clients online and through mobile. That's all metadata. So it's not all whip or stick. There's some real value that is in there as well. >> These would seem to be a domain that is ideal for automation. That through machine learning contextualization machines should be able to figure a lot of this stuff out. Am I wrong? >> No, absolutely right. And I think there's, we're working on proof of concepts to prove that case. And we have IBM AMG as well. The automatic metadata generation capability using machine learning and AI to be able to start to auto-generate some of this insight by using existing catalogs, et cetera et cetera. And we're starting to see real value through that. It's still very early days but I think we're really starting to see that one of the solutions can be machine learning and AI. For sure. >> I think there's various degrees of automation that will come in waves for the next, immediately right now we have certain degrees where we have a very small term set that is very high confidence predictions. But then you want to get specific to the specificity of a company which have 30,000 terms sometimes. Internally, we have 6,000 terms at IBM. And that level of specificity to have complete automation we're not there yet. But it's coming. It's a trial. >> It takes time because the machine is learning. And you have to give the machine enough inputs and gradually take time. Humans are involved as well. It's not about just throwing the machine at something and letting it churn. You have to have that human involvement. It takes time to have the machine continue to learn and grow and give it more terms. And give it more context. But over time I think we're going to see good results. >> I want to ask about that human-in-the-loop as IBM so often calls it. One of the things that Nander Paul Bendery was talking about is how the CDO needs to be a change engine in chief. So how are the rank and file interpreting this move to automation and increase in machine learning in their organizations? Is it accepted? It is (chuckles) it is a source of paranoia and worry? >> I think it's a mix. I think we're kind of blessed at least in the CDO at IBM, the global CDO. Is that everyone's kind of on board for that mission. That's what we're doing >> Right, right. >> There's team members 25, 30 years on IMBs roster and they're just as excited as I am and I've only been there for 16 months. But it kind of depends on the project too. Ones that have a high impact. Everyone's really gung ho because we've seen process times go from 90 days down to a couple of days. That's a huge reduction. And that's the governance regulatory aspects but more for us it's a little bit about we're looking for the linkage and availability of data. So that we can get more insights from that data and better outcomes for different types of enterprise use cases. >> And a more satisfying work day. >> Yeah it's fun. >> That's a key point. Much better to be involved in this than doing the job itself. The job of tagging and creating metadata associated with the vast number of data elements is very hard work. >> Yeah. >> It's very difficult. And it's much better to be working with machine learning to do it and dealing with the outliers or the exceptions than it is chugging through. Realistically it just doesn't scale. You can't do this across 30,000 elements in any meaningful way or a way that really makes sense from a financial perspective. So you really do need to be able to scale this quickly and machine learning is the way to do it. >> Have you found a way to make data governance fun? Can you gamify it? >> Are you suggesting that data governance isn't fun? (all laughing) Yes. >> But can you gamify it? Can you compete? >> We're using gamification in various in many ways. We haven't been using it in terms of data governance yet. Governance is just a horrible word, right? People have really negative connotations associated with it. But actually if you just step one degree away we're talking about quality. Quality means better decisions. And that's actually all governance is. Governance is knowing where your data is. Knowing who's responsible for fixing if it goes wrong. And being able to measure whether it's right or wrong in the first place. And it being better means we make better decisions. Our customers have better engagement with us. We please our customers more and therefore they hopefully engage with us more and buy more services. I think we should that your governance is something we invented through the need for regulation. And the need for control. And from that background. But realistically it's just, we should be proud about the data that we use in the organization. And we should want the best results from it. And it's not about governance. It's about us being proud about what we do. >> Yeah, a great note to end on. Thank you so much Christopher and Steven. >> Thank you. >> Cheers. >> I'm Rebecca Night for Paul Gillen we will have more from the IBM CDO Summit here in Boston coming up just after this. (electronic music)

>> Live from Boston, it's theCUBE! Covering IBM Chief Data Officers Summit. Brought to you by IBM. >> Welcome back to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host Rebecca Knight, along with my co-host Paul Gillin. We're joined by Inderpal Bhandari, he is the Global Chief Data Officer at IBM. Thank you so much for coming back on theCUBE, Inderpal. >> It's my pleasure. >> It's great to have you. >> Thank you for having me. >> So I want to talk, I want to start by talking a little bit about your own career journey. Your first CDO job was in the early 2000s. You were one of the first CDOs, ever. In the history of Chief Data Officers. Talk a little bit about the evolution of the role and sort of set the scene for our viewers in terms of what you've seen, in your own career. >> Yes, no thank you, December 2006, I became a Chief Data Officer of a major healthcare company. And you know, it turned out at that time there were only four of us. Two in banking, one in the internet, I was the only one in healthcare. And now of course there are well over 1,999 of us and the professions taken off. And I've had the fortune of actually doing this four times now. So leading a legacy in four different organizations in terms of building that organizational capability. I think initially, when I became Chief Data Officer, the culture was one of viewing data's exhaust. Something that we had to discard, that came out of the transactions that you were, that your business was doing. And then after that you would discard this data, or you didn't really care about it. And over the course of time, people had begun to realize that data is actually a strategic asset and you can really use it to drive not just the data strategy, but the actual business strategy, and enable the business to go to the next level. And that transitions been tremendous to watch and to see. I've just been fortunate that I've been there for the full journey. >> Are you seeing any consensus developing around what background makes for a good CDO? What are the skills that a CDO needs? >> Yeah, no that's a very, very good question. My view has been evolving on that one too, over the last few years, right, as I've had these experiences. So, I'll jump to the conclusion, so that you kind of, to answer your question as opposed to what I started out with. The CDO, has to be the change agent in chief, for the organization. That's really the role of the CDO. So yes, there's the technical sharps that you have to have and you have to be able to deal with people who have advanced technical degrees and to get them to move forward. But you do have to change the entire organization and you have to be adept at going after the culture, changing it. You can't get frustrated with all the push back, that's inevitable. You have to almost develop it as an art, as you move forward. And address it, not just bottom up and lateral, but also top down. And I think that's probably where the art gets the most interesting. Because you've got to push a for change even at the top. But you can push just so far without really derailing everything that you are trying to do. And so, I think if I have to pick one attribute, it would be that the CDO has to be the change agent in chief and they have to be adept at addressing the culture of the organization, and moving it forward. >> You're laying out all of these sort of character traits that someone has to be indefatigable, inspirational, visionary. You also said during the keynote you have six months to really make your first push, the first six months are so important. When we talk about presidents, it's the first 100 days. Describe what you mean by that, you have six months? >> So if a new, and I'm talking here mainly about a large organization like an IBM, a large enterprise. When you go in, the key observation is it's a functioning organization. It's a growing concern. It's already making money, it's doing stuff like that. >> We hope. >> And the people who are running that organization, they have their own needs and demands. So very quickly, you can just become somebody who ends up servicing multiple demands that come from different business units, different people. And so that's kind of one aspect of it. The way the organization takes over if you don't really come in with an overarching strategy. The other way the organizations take over is typically large organizations are very siloed. And even at the lower levels you who have people who developed little fiefdoms, where they control that data, and they say this is mine, I'm not going to let anybody else have it. They're the only one's who really understand that curve. And so, pretty much unless you're able to get them to align to a much larger cause, you'll never be able to break down those silos, culturally. Just because of the way it's set up. So its a pervasive problem, goes across the board and I think, when you walk in you've got that, you call it honeymoon period, or whatever. My estimate is based on my experience, six months. If you don't have it down in six months, in terms of that larger cause that your going to push forward, that you can use to at least align everybody with the vision, or you're not going to really succeed. You'll succeed tactically, but not in a strategic sense. >> You're about to undertake the largest acquisition in IBM's history. And as the Chief Data Officer, you must be thinking right now about what that's going to mean for data governance and data integration. How are you preparing for an acquisition that large? >> Yeah so, the acquisition is still got to work through all the regulations, and so forth. So there's just so much we can do. It's much more from a planning stand point that we can do things. I'll give you a sense of how I've been thinking about it. Now we've been doing acquisitions before. So in that since we do have a set process for how we go about it, in terms of evaluating the data, how we're going to manage the data and so forth. The interesting aspect that was different for me on this one is I also talked back on our data strategy itself. And tried to understand now that there's going to be this big acquisition of move forward, from a planning standpoint how should I be prepared to change? With regard to that acquisition. And because we were so aligned with the overall IBM business strategy, to pursue cognition. I think you could see that in my remarks that when you push forward AI in a large enterprise, you very quickly run into this multi-cloud issue. Where you've got, not just different clouds but also unprime and private clouds, and you have to manage across all that and that becomes the pin point that you have to scale. To scale you have to get past that pin point. And so we were already thinking about that. Actually, I just did a check after the acquisition was announced, asking my team to figure out well how standardized are we with Red Hat Linux? And I find that we're actually completely standardized across with Red Hat Linux. We pretty much will have use cases ready to go, and I think that's the facet of the goal, because we were so aligned with the business strategy to begin with. So we were discovering that pinpoint, just as all our customers were. And so when the cooperation acted as it did, in some extent we're already ready to go with used cases that we can take directly to our clients and customers. I think it also has to do with the fact that we've had a partnership with Red Hat for some time, we've been pretty strategic. >> Do you think people understand AI in a business context? >> I actually think that that's, people don't really understand that. That's was the biggest, in my mind anyway, was the biggest barrier to the business strategy that we had embarked on several years ago. To take AI or cognition to the enterprise. People never really understood it. And so our own data strategy became one of enabling IBM itself to become an AI enterprise. And use that as a showcase for our clients and customers, and over the journey in the last two, three years that I've been with IBM. We've become more, we've been putting forward more and more collateral, but also technology, but also business process change ideas, organizational change ideas. So that our clients and customers can see exactly how it's done. Not that i'ts perfect yet, but that too they benefit from, right? They don't make the same mistakes that we do. And so we've become, your colleagues have been covering this conference so they will know that it's become more and more clear, exactly what we're doing. >> You made an interesting comment, in the keynote this morning you said nobody understands AI in a business context. What did you mean by that? >> So in a business context, what does it look like? What does AI look like from an AI enterprise standpoint? From a business context. So excuse me I just trouble them for a tissue, I don't know why. >> Okay, alright, well we can talk about this a little bit too while he-- >> Yeah, well I think we understand AI as an Amazon Echo. We understand it as interface medium but I think what he was getting at is that impacting business processes is a lot more complicated. >> Right. >> And so we tend to think of AI in terms of how we relate to technology rather than how technology changes the rules. >> Right and clearly its such, on the consumers side, we've all grasped this and we all are excited by its possibilities but in terms of the business context. >> I'm back! >> It's the season, yes. >> Yeah, it is the season, don't want to get in closer. So to your question with regard to how-- >> AI in a business context. >> AI in a business context. Consumer context everybody understands, but in a business context what does it really mean? That's difficult for people to understand. But eventually it's all around making decisions. But in my mind its not the big decisions, it's not the decisions we going to acquire Red Hat. It's not those decisions. It's the thousands and thousands of little decisions that are made day in and night out by people who are working the rank and file who are actually working the different processes. That's what we really need to go after. And if you're able to do that, it completely changes the process and you're going to get just such a lot more out of it, not just terms of productivity but also in terms of new ideas that lead to revenue enhancement, new products, et cetera, et cetera. That's what a business AI enterprise looks like. And that's what we've been bringing forward and show casing. In today's keynote I actually had Sonya, who is one of our data governance people, SMEs, who works on metadata generation. Really a very difficult manual problem. Data about data, specifically labeling data so that a business person could understand it. Its all been done manually but now it's done automatically using AI and its completely changed the process. But Sonya is the person who's at the forefront of that and I don't think people really understand that. They think in terms of AI and business and they think this is going to be somebody who's a data scientist, a technologist, somebody who's a very talented technical engineer, but it's not that. It's actually the rank and file people, who've been working these business processes, now working with an intelligent system, to take it to the next level. >> And that's why as you've said it's so important that the CDO is a change agent in chief. Because it is, it does require so much buy-in from, as you say, the rank and file, its not just the top decision makers that you're trying to persuade. >> Yes, you are affecting change at all levels. Top down, bottom up, laterally. >> Exactly. >> You have to go after it across the board. >> And in terms of talking about the data, it's not just data for data's sake. You need to talk about it in terms that a business person can understand. During the keynote, you described an earlier work that you were doing with the NBA. Can you tell our viewers a little bit about that? And sort of how the data had to tell a story? >> Yes, so that was in my first go 'round with IBM, from 1990 through '97. I was with IBM Research, at the Watson Research Lab, as a research staff member. And I created this program called Advanced Scout for the National Basketball Association. Ended up being used by every team on the NBA. And it would essentially suggest who to put in the line up, when you're matching lines up and so forth. By looking at a lot of game data and it was particularly useful during the Playoff games. The major lesson that came out of that experience for me, at that time, alright, this was before Moneyball, and before all this stuff. I think it was like '90, '93, '92. I think if you Google it you will still see articles about this. But the main lesson that came out for me was the first time when the program identified a pattern and suggested that to a coach during a playoff game where they were down two, zero, it suggested they start two backup players. And the coach was just completely flabbergasted, and said there's no way I'm going to do this. This is the kind of thing that would not only get me fired, but make me look really silly. And it hit me then that there was context that was missing, that the coach could not really make a decision. And the way we solved it then was we tied it to the snippets of video when those two players were on call. And then they made the decision that went on and won that game, and so forth. Today's AI systems can actually fathom all that automatically from the video itself. And I think that's what's really advanced the technology and the approaches that we've got today to move forward as quickly as they have. And they've taken hold across the board, right? In the sense of a consumer setting but now also in the sense of a business setting. Where we're applying it pretty much to every business process that we have. >> Exciting. Well Inderpal, thank you so much for coming back on theCUBE, it was always a pleasure talking to you. >> It's my pleasure, thank you. >> I'm Rebecca Knight for Paul Gillin, we will have more from theCUBE's live coverage of IBM CDO coming up in just a little bit. (upbeat music)

>> Live from Boston, it's theCUBE! Covering IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back everyone to the IBM CDO Summit and theCUBE's live coverage, I'm your host Rebecca Knight along with my co-host Paul Gillin. We have Joe Selle joining us. He is the Cognitive Solution Lead at IBM. And Thomas Ward, Supply Chain Cloud Strategist at IBM. Thank you so much for coming on the show! >> Thank you! >> Our pleasure. >> Pleasure to be here. >> So, Tom, I want to start with you. You are the author of Risk Insights. Tell our viewers a little bit about Risk Insights. >> So Risk Insights is a AI application. We've been working on it for a couple years. What's really neat about it, it's the coolest project I've ever worked on. And it really gets a massive amount of data from the weather company, so we're one of the biggest consumers of data from the weather company. We take that and we'd visualize who's at risk from things like hurricanes, earthquakes, things like IBM sites and locations or suppliers. And we basically notify them in advance when those events are going to impact them and it ties to both our data center operations activity as well as our supply chain operations. >> So you reduce your risk, your supply chain risk, by being able to proactively detect potential outages. >> Yeah, exactly. So we know in some cases two or three days in advance who's in harm's way and we're already looking up and trying to mitigate those risks if we need to, it's going to be a real serious event. So Hurricane Michael, Hurricane Florence, we were right on top of it and said we got to worry about these suppliers, these data center locations, and we're already working on that in advance. >> That's very cool. So, I mean, how are clients and customers, there's got to be, as you said, it's the coolest project you've ever worked on? >> Yeah. So right now, we use it within IBM right? And we use it to monitor some of IBM's client locations, and in the future we're actually, there was something called the Call for Code that happened recently within IBM, this project was a semifinalist for that. So we're now working with some non-profit groups to see how they could also avail of it, looking at things like hospitals and airports and those types of things as well. >> What other AI projects are you running? >> Go ahead. >> I can answer that one. I just wanted to say one thing about Risk Insights, which didn't come out from Tom's description, which is that one of the other really neat things about it is that it provides alerts, smart alerts out to supply chain planners. And the alert will go to a supply chain planner if there's an intersection of a supplier of IBM and a path of a hurricane. If the hurricane is vectored to go over that supplier, the supply chain planner that is responsible for those parts will get some forewarning to either start to look for another supplier, or make some contingency plans. And the other nice thing about it is that it launches what we call a Resolution Room. And the Resolution Room is a virtual meeting place where people all over the globe who are somehow impacted by this event can collaborate, share documents, and have a persistent place to resolve this issue. And then, after that's all done, we capture all the data from that issue and the resolution and we put that into a body of knowledge, and we mine that knowledge for a playbook the next time a similar event comes along. So it's a full-- >> It becomes machine learning. >> It's a machine learning-- >> Sort of data source. >> It's a full soup to nuts solution that gets smarter over time. >> So you should be able to measure benefits, you should have measurable benefits by now, right? What are you seeing, fewer disruptions? >> Yes, so in Risk Insights, we know that out of a thousand of events that occurred, there were 25 in the last year that were really the ones we needed to identify and mitigate against. And out of those we know there have been circumstances where, in the past IBM's had millions of dollars of losses. By being more proactive, we're really minimizing that amount. >> That's incredible. So you were going to talk about other kinds of AI that you run. >> Right, so Tom gave an overview of Risk Insights, and we tied it to supply chain and to monitoring the uptime of our customer data centers and things like that. But our portfolio of AI is quite broad. It really covers most of the middle and back and front office functions of IBM. So we have things in the sales domain, the finance domain, the HR domain, you name it. One of the ones that's particularly interesting to me of late is in the finance domain, monitoring accounts receivable and DSO, day sales outstanding. So a company like IBM, with multiple billions of dollars of revenue, to make a change of even one day of day sales outstanding, provides gigantic benefit to the bottom line. So we have been integrating disparate databases across the business units and geographies of IBM, pulling that customer and accounts receivable data into one place, where our CFO can look at an integrated approach towards our accounts receivable and we know where the problems are, and we're going to use AI and other advanced analytic techniques to determine what's the best treatment for that AI, for those customers who are at risk because of our predictive models, of not making their payments on time or some sort of financial risk. So we can integrate a lot of external unstructured data with our own structured data around customers, around accounts, and pull together a story around AR that we've never been able to pull before. That's very impactful. >> So speaking of unstructured data, I understand that data lakes are part of your AI platform. How so? >> For example, for Risk Insights, we're monitoring hundreds of trusted news sources at any given time. So we know, not just where the event is, what locations are at risk, but also what's being reported about it. We monitor Twitter reports about it, we monitor trusted news sources like CNN or MSNBC, or on a global basis, so it gives our risk analyst not just a view of where the event is, where it's located, but also what's being said, how severe it is, how big are those tidal waves, how big was the storm surge, how many people were affected. By applying some of the machine learning insights to these, now we can say, well if there are couple hundred thousand people without power then it's very likely there is going to be multimillions of dollars of impact as a result. So we're now able to correlate those news reports with the magnitude of impact and potential financial impact to the businesses that we're supporting. >> So the idea being that IBM is saying, look what we've done for our own business (laughs), imagine what we could do for you. As Inderpal has said, it's really using IBM as its own test case and trying to figure this all out and learning as it goes and he said, we're going to make some mistakes, we've already made some mistakes but we're figuring it out so you don't have to make those mistakes. >> Yeah that's right. I mean, if you think about the long history of this, we've been investing in AI, really, since, depending on how you look at it, since the days of the 90's, when we were doing Deep Blue and we were trying to beat Garry Kasparov at chess. Then we did another big huge push on the Jeopardy program, where we we innovated around natural language understanding and speed and scale of processing and probability correctness of answers. And then we kind of carry that right through to the current day where we're now proliferating AI across all of the functions of IBM. And there, then, connecting to your comment, Inderpal's comment this morning was around let's just use all of that for the benefit of other companies. It's not always an exact fit, it's never an exact fit, but there are a lot of pieces that can be replicated and borrowed, either people, process or technology, from our experience, that would help to accelerate other companies down the same path. >> One of the questions around AI though is, can you trust it? The insights that it derives, are they trustworthy? >> I'll give a quick answer to that, and then Tom, it's probably something you want to chime in on. There's a lot of danger in AI, and it needs to be monitored closely. There's bias that can creep into the datasets because the datasets are being enhanced with cognitive techniques. There's bias that can creep into the algorithms and any kind of learning model can start to spin on its own axis and go in its own direction and if you're not watching and monitoring and auditing, then it could be starting to deliver you crazy answers. Then the other part is, you need to build the trust of the users, because who wants to take an answer that's coming out of a black box? We've launched several AI projects where the answer just comes out naked, if you will, just sitting right there and there's no context around it and the users never like that. So we've understood now that you have to put the context, the underlying calculations, and the assessment of our own probability of being correct in there. So those are some of the things you can do to get over that. But Tom, do you have anything to add to that? >> I'll just give an example. When we were early in analyzing Twitter tweets about a major storm, what we've read about was, oh, some celebrity's dog was in danger, like uh. (Rebecca laughs) This isn't very helpful insight. >> I'm going to guess, I probably know the celebrity's dog that was in danger. (laughs) >> (laughs) actually stop saying that. So we learned how to filter those things out and say what are the meaningful keywords that we need to extract from and really then can draw conclusions from. >> So is Kardashian a meaningful word, (all laughing) I guess that's the question. >> Trending! (all laughing) >> Trending now! >> I want to follow up on that because as an AI developer, what responsibility do developers have to show their work, to document how their models have worked? >> Yes, so all of our information that we provided the users all draws back to, here's the original source, here's where the information was taken from so we can draw back on that. And that's an important part of having a cognitive data, cognitive enterprise data platform where all this information is stored 'cause then we can refer to that and go deeper as well and we can analyze it further after the fact, right? You can't always respond in the moment, but once you have those records, that's how you can learn from it for the next time around. >> I understand that building test models in some cases, particularly in deep learning is very difficult to build reliable test models. Is that true, and what progress is being made there? >> In our case, we're into the machine learning dimension yet, we're not all the way into deep learning in the project that I'm involved with right now. But one reason we're not there is 'cause you need to have huge, huge, vast amounts of robust data and that trusted dataset from which to work. So we aspire towards and we're heading towards deep learning. We're not quite there yet, but we've started with machine learning insights and we'll progress from there. >> And one of the interesting things about this AI movement overall is that it's filled with very energetic people that's kind of a hacker mindset to the whole thing. So people are grabbing and running with code, they're using a lot of open source, there's a lot of integration of the black box from here, from there in the other place, which all adds to the risk of the output. So that comes back to the original point which is that you have to monitor, you have to make sure that you're comfortable with it. You can't just let it run on its own course without really testing it to see whether you agree with the output. >> So what other best practices, there's the monitoring, but at the same time you do that hacker culture, that's not all bad. You want people who are energized by it and you are trying new things and experimenting. So how do you make sure you let them have, sort of enough rein but not free rein? >> I would say, what comes to mind is, start with the business problem that's a real problem. Don't make this an experimental data thing. Start with the business problem. Develop a POC, a proof of concept. Small, and here's where the hackers come in. They're going to help you get it up and running in six weeks as opposed to six months. And then once you're at the end of that six-week period, maybe you design one more six-week iteration and then you know enough to start scaling it and you scale it big so you've harnessed the hackers, the energy, the speed, but you're also testing, making sure that it's accurate and then you're scaling it. >> Excellent. Well thank you Tom and Joe, I really appreciate it. It's great to have you on the show. >> Thank you! >> Thank you, Rebecca, for the spot. >> I'm Rebecca Knight for Paul Gillin, we will have more from the IBM CDO summit just after this. (light music)

>> Live from Boston, it's theCUBE, covering IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back everyone to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host Rebecca Knight*, and I'm joined by cohost, Paul Gillan*. We have a guest today, John Thomas. He is the Distinguished Engineer and Director* at IBM. Thank you so much for coming, returning to theCUBE. You're a CUBE veteran, CUBE alum. >> Oh thank you Rebecca, thank you for having me on this. >> So tell our viewers a little bit about, you're a distinguished engineer. There are only 672 in all of IBM. What do you do? What is your role? >> Well that's a good question. Distinguished Engineer is kind of a technical executive role, which is a combination of applying the technology skills, as well as helping shape IBM strategy in a technical way, working with clients, et cetera. So it is a bit of a jack of all trades, but also deep skills in some specific areas, and I love what I do (laughs lightly). So, I get to work with some very talented people, brilliant people, in terms of shaping IBM technology and strategy. Product strategy, that is part of it. We also work very closely with clients, in terms of how to apply that technology in the context of the client's use status. >> We've heard a lot today about soft skills, the importance of organizational people skills to being a successful Chief Data Officer, but there's still a technical component. How important is the technical side? What is, what are the technical skills that the CDOs need? >> Well, this is a very good question Paul. So, absolutely, so, navigating the organizational structure is important. It's a soft skill. You are absolutely right. And being able to understand the business strategy for the company, and then aligning your data strategy to the business strategy is important, right? But the underlying technical pieces need to be solid. So for example, how do you deal with large volumes of different types of data spread across a company? How do you manage that data? How do you understand the data? How do you govern that data? How do you then master leveraging the value of that data in the context of your business, right? So an understanding, a deep understanding of the technology of collecting, organizing, and analyzing that data is needed for you to be a successful CDO. >> So in terms of, in terms of those skillsets that you're looking for, and one of the things that Inderpal said earlier in his keynote, is that, there are just, it's a rare individual who truly understands the idea of how to collect, store, analyze, curatize, monetize the data, and then also have the soft skills of being able to navigate the organization, being able to be a change agent who is inspiring, inspiring the rank and file. How do you recruit and retain talent? I mean, this seems to be a major challenge. >> Expertise is, and getting the right expertise in place, and Inderpal talked about it in his keynote, which was the very first thing he did was bring in talent. Sometimes it is from outside of your company. Maybe you have a kind of talent that has grown up in your company. Maybe you have to go outside, but you've got to bring in the right skills together. Form the team that understands the technology, and the business side of things, and build this team, and that is essential for you to be a successful CDO. And to some extent, that's what Inderpal has done. That's what the analytic CDO's office has done. Seth Dobrin, my boss, is the analytics CDO , and he and the analytics CDO team actually hired people with different skills. Data engineering skills, data science skills, visualization skills, and then put this team together which understands the, how to collect, govern, curate, and analyze the data, and then apply them in specific situations. >> There's been a lot of talk about AI, at this conference, which seems to be finally happening. What do you see in the field, or perhaps projects that you've worked on, of examples of AI that are really having a meaningful business impact? >> Yeah Paul, that is a very good question because, you know, the term AI is overused a lot as you can imagine, a lot of hype around it. But I think we are past that hype cycle, and people are looking at, how do I implement successful use cases? And I stress the word use case, right? In my experience these, how I'm going to transform my business in one big boil the ocean exercise, does not work. But if you have a very specific bounded use case that you can identify, the business tells you this is relevant. The business tells you what the metrics for success are. And then you focus your attention, your efforts on that specific use case with the skills needed for that use case, then it's successful. So, you know, examples of use cases from across the industries, right? I mean everything that you can think of. Customer-facing examples, like, how do I read the customer's mind? So when, if I'm a business and I interact with my customers, can I anticipate what the customer is looking for, maybe for a cross-sell opportunity, or maybe to reduce the call handing time when a customer calls into my call center. Or trying to segment my customers so I can do a proper promotion, or a campaign for that customer. All of these are specific customer phasing examples. There also are examples of applying this internally to improve precesses, capacity planning for your infrastructure, can I predict when a system is likely to have an outage, or can I predict the traffic coming into my systems, into my infrastructure and provision capacity for that on demand, So all of these are interesting applications of AI in the enterprise. >> So when your trying, what are the things we keep hearing, is that we need to data to tell a story To, the data needs to be compelling enough so that the people, the data scientist get it but then also the other kinds of business decision makers get it to. >> Yep >> So, what are sort of, the best practices that have emerged from your experience? In terms of, being able to, for your data to tell a story that you want it to tell. >> Yeah, well I mean if the pattern doesn't exist in the data then no amount of fancy algorithms can help, you know? and sometimes its like searching for a needle in a haystack but assuming, I guess the first step is, like I said, What is the use case? Once you have a clear understanding of your use case and such metrics for your use case, do you have the data to support that use case? So for example if it's fraud detection, do you actually have the historical data to support the fraud use case? Sometimes you may have transactional data from your, transocular from your core enterprise systems but that may not be enough. You may need to alt mend it with external data, third party data, maybe unstructured data, that goes along with your transaction data. So the question is, can you identify the data that is needed to support the use case and if so can I, is that data clean, is that data, do you understand the lineage of the data, who has touched and modified the data, who owns the data. So then I can start building predictive models and machine learning, deep learning models with that data. So use case, do you have the data to support the use case? Do you understand how that sata reached you? Then comes the process of applying machine learning algorithms and deep learning algorithms against that data. >> What are the risks of machine learning and particularly deep learning, I think because it becomes kind of a black box and people can fall into the trap of just believing what comes back, regardless of whether the algorithms are really sound or the data is. What is the responsibility of data scientist to sort of show their work? >> Yeah, Paul this is fascinating and not completely solid area, right? So, bias detection, can I explain how my model behaved, can I ensure that the models are fair in their predictions. So there is a lot of research, a lot of innovation happening in the space. IBM is investing a lot into space. We call trust and transparency, being able to explain a model, it's got multiple levels to it. You need some level of AI governments itself, just like we talked about data governments that is the notion of AI governments. Which is what motion of the model was used to make a prediction? What were the imports that went into that model? What were the decisions that were, that were the features that were used to make a sudden prediction? What was the prediction? And how did that match up with ground truth. You need to be able to capture all that information but beyond that, we have got actual mechanisms in place that IBM Research is developing to look at bias detection. So pre processing during execution post processing, can I look for bias in how my models behave and do I have mechanisms to mitigate that? So one example is the open source Python library, called AIF360 that comes from IBM Research and has contributed to the open source community. You can look at, there are mechanisms to look at bias and provide some level of bias mitigation as part of your model building exercises. >> And the bias mitigation, does it have to do with, and I'm going to use an IMB term of art here, the human in the loop, is it how much are you actually looking at the humans that are part of this process >> Yeah, humans are at least at this point in time, humans are very much in the loop. This notion of Peoria high where humans are completely outside the loop is, we're not there yet so very much something that the system can for awhile set off recommendations, can provide a set of explanations and can someone who understands the business look at it and make a corrective, take corrective actions. >> There has been, however to Rebecca's point, some prominent people including Bill Gates, who have speculated that the AI could ultimately be a negative for humans. What is the responsibility of company's like IBM to ensure that humans are kept in the loop? >> I think at least at this point IBM's view is humans are an essential part of AI. In fact, we don't even use artificial intelligence that much we call it augmented intelligence. Where the system is pro sending a set of recommendations, expert advise to the human who can then make a decision. For example, you know my team worked with a prominent health care provider on you know, models for predicting patient death in the case of sepsis, sepsis-onset. This is, we are talking literally life and death decisions being made and this is not something you can just automate and throw into a magic black box, and have a decision be made. So this is absolutely a place where people with deep, domain knowledge are supported, are opt mended with, with AI to make better decisions, that's where I think we are today. As to what will happen five years from now, I can't predict that yet. >> Well I actually want to- >> But the question >> bring this up to both of you, the role, so you are helping doctor's make these decisions, not just this is what the computer program says about this patient's symptoms here but this is really, so you are helping the doctor make better decisions. What about the doctors gut, in the, his or her intuition to. I mean, what is the role of that, in the future? >> I think it goes away, I mean I think, the intuition really will be trumped by data in the long term because you can't argue with the facts. Some people do these days. (soft laughter) But I don't remember (everyone laughing) >> We have take break there for some laughter >> Intrested in your perspective onthat is there, will there, should there always be a human on the front line, who is being supported by the back end or would you see a scenario were an AI is making decisions, customer facing decisions that are, really are life and death decisions? >> So I think in the consumer invest way, I can definitely see AI making decisions on it's own. So you know if lets say a recommender system would say as you know I think, you know John Thomas, bought these last five things online. He's likely to buy this other thing, let's make an offer to him. You know, I don't need another human in the loop for that >> No harm right? >> Right. >> It's pretty straight forward, it's already happening, in a big way but when it comes to some of these >> Prepoping a mortgage, how about that one? >> Yeah >> Where bias creeps in a lot. >> But that's one big decision. >> Even that I think can be automated, can be automated if the threshold is set to be what the business is comfortable with, were it says okay, above this probity level, I don't really need a human to look at this. But, and if it is below this level, I do want someone to look at this. That's you know, that is relatively straight forward, right? But if it is a decision about you know life or death situation or something that effects the very fabric of the business that you are in, then you probably want a domain explore to look at it. In most enterprises, enterprises cases will fall, lean toward that category. >> These are big questions. These are hard questions. >> These are hard questions, yes. >> Well John, thank you so much for doing >> Oh absolutely, thank you >> On theCUBE, we really had a great time with you. >> No thank you for having me. >> I'm Rebecca Knight for Paul Gillan, we will have more from theCUBE's live coverage of IBM CDO, here in Boston, just after this. (Upbeat Music)