 Live from San Francisco, California, it's theCUBE covering the IBM Chief Data Officer 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 gather these panels. This is the data all-star panel and I've recruited Seth Dobrin, who is in 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. Yeah, 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 CDO summit, so two a year, so we've been doing this for five years. The data all-stars are those people that have been to four, at least, of the eight. Or yeah, of the 10, I mean, I'm sorry. And so these are five of the 16 people that got the award. And so thank you all for participating and I know I attended these, like I said earlier, before I joined IBM, they were immensely valuable to me and it's glad to see 16 other people that think it's valuable too. That's 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, you know, what role you play, how you're using data, however you want to frame that. And I want to ask you, 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, you know, that's fine too, you choose. So let me, 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 tell 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, 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, a 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, that'd be fun to mix that up too. Yeah, so I mean the way I would pick it is I'm responsible for the data 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 with 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 a $8 billion 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. But the solution got really implemented. I mean that was a big day 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 as 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 regulatory 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 that we have. And we'll get into a lot of that. So Carl Gold is the Chief Data Scientist at Zuora. To Carl Gold, 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 like 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 don't have to make up your own mind. My role is an interesting one. It's split about, 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 data set created from 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 records that you were depending on finding, remove, yeah, pipeline failures. And a bad day is, yeah, 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, 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 would 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 you 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 really, this is really personal to me because my son has food allergies and he's been to the ER four times. And one of the scariest events was when he went to an ER out of the country. And as a parent, you prepare your child, right? With the food, he takes the food, he was 13 years old and you have the chaperones, everyone all set up. But then 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 that 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 and then you watch the data, obviously. 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 CEO? I'm the de facto. So my career has been pretty varied. So I've been essentially a 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 it's 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, right? 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. Roland Ho is the Vice President of Data and Analytics at Clover Health. Tell us a little bit about Clover Health and then your role. 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 co-pay 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 a Medicare Advantage plan it's limited to like $5,000, $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 and analytics to healthcare, right? So I've been in the 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 and 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. If you had to pay $1,000 to intervene but you're only 20% of the chance to write that's very expensive for each success but now if your model is like 60, 70% right then now you can open 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 called me up and said, hey Roland, your analytics brought us over $100 million in new revenue last year. I was like, ching, excellent. Wish it was a mic. How about a big mic? That's it. Yeah, and then on the data geek side the best day was really run a model, you train a model, you get ridiculous, OXCOR, 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 OXCOR held up through production. I was like, holy cow, the model actually works. 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 a CDO at IBM, because at IBM I spend most of my time traveling and so a good day as a day I'm home. Yeah, when you're not in an aluminum tube. Hurdling through space. No, but a good day is when GDPR compliance just happened. Good day for me was May 20th of last year when IBM was done and we were 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, so. 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 10 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. Oh my gosh, you're boring. Classy. No, but actually. 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 with constantly improving the business which is the first goal, right? Increasing market share through insights and business intelligence, tracking product performance, right? To really helping us respond to regulators. So it's a variety of areas I had to be involved in. So one company, 50 jobs. Exactly, so right now I wear different hats depending on the day, right? So that's really what's... So it's a good question. Have you guys been jumping around? Sure, I mean, I think same company. One company, but two jobs. And I think those two jobs had two different flavors. When I started at Macassan, I was a solution leader or solution rector 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 AI. My new role is actually focused around machine learning and AI. That's why we created this layer around data product owners who understand the data science side of things and we all go and pitch in this architecture. So same company, but I've 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 Zora. 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 Zora I was really focused on subscriber analytics in churn for my first couple of years. And then actually I kind of got a new job at Zora by becoming like the subscription economy expert or subscription, I become like an economist, even though I don't honestly have a background, my PhDs in biology, but you know, now I'm a subscription economy guru and a book author. I'm writing a book, you know, about my experiences in the area. Awesome, that's great. All right, I'll give a, you know, a bit of a riddle. Four, how do you have four jobs, five companies? In five years. In five years. A series of acquisition, acquisition, acquisition. Exactly. So yeah, I've had, I was like, I have to really, really count on that one. Yeah, so I've been with three companies for the past five years and I would say I've had seven jobs. But what's interesting is it's actually kind of, 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, really 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, right? 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, right? 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 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 is the only commodity that increases value when you use it correctly. Yeah, so then that kind of leads to a question that I had is, you know, there's this mantra, the more data, the better, or is it more like an Einstein derivative? Collect as much data as possible, but not too much. What are your thoughts on is more data better? So I would say like that, the curve has shifted over the years, right? So before it used to be data was the bottleneck, right? But now as, you know, especially over the last, you know, five to 10 years, I feel like data is no longer oftentimes the bottleneck as much as the use case, right? 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, right? And then, you know, in the cases where there's not data, like you mechanical turkey, you can like, you know, set up experiments, gather data. The cost of that is now so cheap to experiment that I think the bottlenecks really around the business understanding of the use case. And I think the way 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 with where it is not good is, I think where quality is more required is the area where more data is not good. For example, like regulatory and compliance. So for example, in McCassan'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. Their 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 our 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 model. So there, you could say more data is good. Well, you could definitely see a lot of cases, certainly in financial services and a lot of healthcare, particularly in pharmaceutical, where you don't want work in process hanging around. Some lawyer could find a smoking gun and say, Lucie, and then if that data doesn't get deleted. So Lucie, 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 legal requirements to comply or to answer questions. So we do keep. 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. 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? Like maybe go back 10 years, maybe even 15 years ago, where you didn't have the tooling to be able to say, give me more data, I'll get you the answer. Maybe, I'll give you more data, I'll get the answer in three years. Whereas today, you're able to. Let me go get it off the backup tapes. Yeah, yeah, right, exactly. But fortunately for us, Wells Fargo has implemented data warehouse for so many number of years, I think more than 10 years, right? So we do have that capability. There's certainly a lot of platforms you have to navigate through, but if you're able to navigate, you can get to the data within the required timeline. So as long as you have the technology team behind you. 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 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 record systems, for example, right? 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 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? I want to keep everything. But the most interesting insights often come from small data, which are a subset of that big, that larger keep everything inclination that we as data geeks have. I think also as we're moving into 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 that insight. So I think in my perspective, the answer is yes. Yeah, right. Okay, go. I'll go with that just to run with that question. I think it's a little bit of both because 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 Roland'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, you know, you have a theory of how the thing works and you collect data around that theory. And I think the approach of just, oh, let's put in 2000 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 because. Absolutely. Right, but of course, Carl, in your world, you don't have to guess anymore, right? Cause you have real data to. 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 churned by counting how many times they log in or more crude measures of engagement. All right guys, we got to go. The keynotes are spilling out. Seth, thank you so much. Folks, thank you. I know, I'd love to carry on, right? This is, it goes fast, but guys, great, great content. Really appreciate it. Yeah, thanks and congratulations on participating and being Data All-Stars. Would 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.