>> Narrator: Live from Atlanta, Georgia, it's theCUBE, covering Ansible Fest 2019. Brought to you by Red Hat. >> Welcome back, this is theCUBE's live coverage of Ansible Fest 2019, here in Atlanta, Gerogia. I'm Stu Miniman, my co-host is John Furrier and we're going to dig in and talk a bit about developers. Our guest on the program, Parag Dave, who is senior principle product manager with Red Hat. Thank you so much for joining us. >> Glad to be here, thanks for having me. >> Alright, so configuration management, really maturing into an entire automation journey for customers today, lets get into it. Tell us a little bit about your role and what brings you to the event. >> Yeah, so I actually have a very deep background in automation. I started by doing worker automation. Which is basically about how to help businesses do their processing. So, from processing an invoice, how do I create the flows to do that? And we saw the same thing, like automation was just kind of like a an operational thing and was brought on just to fulfill the business, make it faster and next thing you know it grew like, I don't know, like wildfire. I mean it was amazing and we saw the growth, and people saw the value, people saw how easy it was to use. Now, I think that combination is kicking in. So, now I'm focusing more on developers and the depth tools used at Red Hat and it's the same thing. You know, Parag, you know when you look in IT, you know Automation is not a new term. It's like we've been talking about this for decades. Talk to us a little bit about how it's different today and you know, you talked about some of the roles that are involved here, how does Ansible end up being a developer tool? >> Yeah, you know you see, it's very interesting, because Ansible was never really targeted for developers, right? And in fact, automation was always considered like an operational thing. Well, now what has happened is, the entire landscape of IT in a company is available to be executed programmatically. Before it was, interfaces were only available for a few programs. Everything else you had to kind of write your own programs to do, but now the advent of API's, you know with really rich CLI's it's very easy to interact with anything and not just like in software, you can interact with the other network devices, with your infrastructure, with your storage devices. So, all of the sudden when everything became available, developers who were trying to create applications and needed environments to test, to integrate, saw that automation is a great way to create something that cannot be replicated and be consistent every time you run it. So, the need for consistency and replication drove developers to adopt to the Ansible. And we were, you know cause they had the Ansible, we never marketed to developer and then we see that wow, they are really pulling it down, it's great. The whole infrastructure is code, which is one of the key pillars for devOps has become one of the key drivers for it, because now what you are seeing is the ability for developers to say that I can now, when I'm done with my coding and my application is ready for say a test environment or a staging environment, I can now provision everything I need right from configuring my network devices, getting the infrastructure ready for it, run my test, bring it down, and I can do all of that through code, right? So, that really drives the adoption for Ansible. >> And the could scale has shown customers at scale, whether its on-premises or cloud or Edge is really going to be a big factor in their architecture. The other thing that's interesting, and Stu were talking about this on our opening yesterday, is that you have the networking and the bottom of that stack moving up the stack and you have the applications kind of wanting to move down the stack. So, they're kind of meeting in the middle in this programmability in between them. You know, Containers, Kubernetes, Microservices, is developing as a nice middle layer between those two worlds. So, the networks have to telegraph up data and also be programmable, this is causing a lot of disruption and evasion. >> Parag: Absolutely. >> You're thought on this, 'cause it's DevSecOps beefs DevOps, that's DeVops. This is now all that's coming together. Exactly, and what's happening is, what we are seeing with developers is that there's a lot more empowerment going on. You know, before there was like a lot of silo's, there was like a lot of checks and balances in place that kind of made it hard to do things. It was okay, this what you, developers you write code, we will worry about all this. And now, this whole blending that has happened and developers being empowered to do it. And now, the empowerment is great and with great power comes great responsibility. SO, can you please make sure that you know, what you're using is enterprise grade, that it's going to be you know, you're not just doing things with your break environment So, once everybody become comfortable that yes, by merging these things together, we're actually not breaking things. You're actually increasing speed, 'cause what's the number one driver right now for organizations? Is speed with security, right? Can I achieve that business agility, so that by the time I need a feature develop, by the time I need a feature delivered in production and my tool comes for it, I need to close that gap. I cannot have a long gap between that. So, we are seeing a lot of that happening. >> People love automation, they love AI. These are two areas that, it's a no-brainer. When you have automation, you talk AI, yeah bring it on, right? What does that mean? So, when you think about automation the infrastructure that's in the hands of the operators, but also they want to enable applications to do it themselves as well, hence the DevOps. Where is the automation focus? Because that's the number one question. How do I land, get the adoption, and then expand out across. This seems to be the form that Ansible's kind of cracked the code on. The organic growth has been there, but now as a large enterprise comes in, I got to get the developers using it and it's got to be operator friendly. This seems to be the key, >> The balance has to be there >> the key to the kingdom. >> Yeah, no you're absolutely right. And so, when you look at it, like what do developers want? So, something that is frictionless to use, very quick, very easy, and so that I don't have to spend a lot of time learning it and doing it, right? And so we saw that with Ansible. It's like the fact that it's so easy to use, it's most of everything is in YAML. Which is very needed for developers, right? So, we see that from their perspective, they're very eager now, and they've been adopting it, if you look at the download stats it tells you. Like there's a lot of volume happening in terms of developers adopting it. What companies are now noticing is that, wait that's great, but now we have a lot developers doing their own thing. So, there is now like way of bringing all this together, right? So, it's like if I have 20 teams in one line of business and each team tries to do things their own way, what I'm going to end up with is a lot of repeatable, you know like a lot of work that gets repeated, I say it's duplicated. So, we see that's what we are seeing with collections for example. What Ansible is trying to bring to the table is okay, how do I help you kind of bring things into one umbrella? And how can I help you as a developer decide that, wow I got like 100 plus engine extra rolls I can use in Ansible. Well, which one do I pick? And you pick one, somebody else picks something else, Somebody creates a playbook with like one separate, you know one different thing in it, versus yours. How do we get our hands around it? And I think that's where we are seeing that happen. >> Right open star standpoint. I see Red Hat, Ansible doing great stuff and for the folks in the ivory tower, the executive CXO'S. They hear Ansible, glue layer, integration layer, and they go, wait a minute isn't that Kubernetes? Isn't Kubernetes suppose to provide all this stuff? So, talk about where Ansible fits in the wave that's coming with Kubernetes. Pat Gelsinger at VMware, thinks Kubernetes is going to be the dial-tone, it's going to be like the TCP/IP like protocol, to use his words, but there's a relationship that Ansible has with those Microservices that are coming. Can you explain that fit? >> You hit the nail on the head. Like, Kubernetes is like, we call it the new operating system. It's like that's what everything runs on now, right? And it's very easy for us, you know from a development perspective to say, great I have my Containers, I have my applications built, I can bring them up on demand, I don't have to worry about you know having the whole stack of an operating system delivered every time. So, Kubernetes has become like the defactual standard upon which things run. So, one of the concepts that has really caught a lot of momentum, is the operator framework, right? Which was introduced with the Kubernetes, the later Razor 3.x. Some of that, and operator framework, it's very easy now for application teams. I mean, it's not a great uptake from software vendors themselves. How do I give you my product, that you can very easily deliver on Kubernetes as a Container, but I'll give you enough configuration options, you can make it work the way you want to. So, we saw a lot oof software vendors creating and delivering their products as operators. Now we are seeing that a lot of software application developers themselves, for their own applications, want to create operators. It's a very easy way of actually getting your application deployed onto Kubernetes. So, Ansible operator is one of the easiest ways of creating an operator. Now, there are other options. You can do a Golang operator, you can do Helm, but Ansible operators has become extremely easier to get going. It doesn't require additional tools on top of it. Just because the operator SDK, you know, you're going to use playbooks. Which you're used to already and you're going to use playbooks to execute your application workflows. So, we feel that developers are really going to use Ansible operators as a way to create their own operators, get it out there, and this is true for any Kubernetes world. So, there's nothing different about, you know an Ansible operator versus any other operator. >> With no chains to Kubernetes, but Kubernetes obviously has the cons of the Microservices, which is literally non-user intervention. The apps take of all provisioning of services. This is an automation requirement, this feeds into the automation theme, right? >> Exactly, and what this does for you is it helps you, like if you look at operator framework, it goes all the way from basic deployers, everybody's use to, like okay, I want instantaneous deployment, automatically just does it. Automatically recognize changes that I give you in reconfiguration and go redeploy a new instance the way it should. So, how do I automate that? Like how do I ensure that my operator that is actually running my application can set up it's own private environment in Kubernetes and then it can actually do it automatically when I say okay now go make one change to it. Ansible operator allows you to do that and it goes all the way into the life cycle, the full five phases of life cycle that we have in the operator framework. Which is the last one's about autopilot. So, Autoscale, AutoRemedy itself. Your application now on Kubernetes through Ansible can do all that and you don't have to worry about coding at all. It's all provided to you because of the Ansible operator. >> Parag, in the demo this morning, I think the audience really, it resonated with the audience, it talked about some of the roles and how they worked together and it was kind of, okay the developers on this side and the developers expectation is, oh the infrastructure's not going to be ready, I'm not going to have what I need. Leave me alone, I'm going to play my video games until I can actually do my work and then okay, I'll get it done and do my magic. Speak a little bit to how Ansible is helping to break through those silo's and having developers be able to fully collaborate and communicate with all their other team members not just be off on their own. >> Oh yeah, that's a good point, you know. And what is happening is the developers, like what Ansible is bringing to the table is giving you a very prescriptive set of rules that you can actually incorporate into your developer flows. So, what developers are now doing is that I can't create a infrastructure contribution without actually having discussions with the infrastructure folks and the network team will have to share with me what is the ideal contribution I should be using. So, the empowerment that Ansible brings to the table is enabled cross team communications to happen. So, there is prescriptive way of doing things and you can create this all into an automation and then just set up so that it gets triggered every time a developer makes a change to it. So, internally they do that. Now other teams come and say, hey how are you doing this? Right, 'cause they need they same thing. Maybe you're destinations are going to be different obviously, but in the end the mechanism is the same, because you are under the same enterprise, right? So, you're going to have the same layer of network tools, same infrastructure tools. So, then teams start talking to each other. I was talking to the customer and they were telling me that they started with four teams working independently, building their own Ansible playbooks and then talking to the admins and next thing they know everybody had the full automation done and nobody knew about it. And now they're finding out and they were saying, wow, I got like hundreds of these teams doing this. So, A, I'm very happy, but B, now I would like these guests to talk to each other more and come up with a standard way of doing it. And going back to that collections concept. That's what's really going to help them. And we feel that with the collections it's very similar to what we did with Operator Hub for the OpenShift. It's where we have certified set of collections, so that they're supported by Red Hat. We have partners who contribute theirs and then they're supported by them, but we become a single source. So, as an enterprise you kind of have this way of saying, okay now I can feel confident about what I'm going to let you deploy in my environment and everybody's going to follow the same script and so now I can open up the floodgates in my entire organization and go for it. >> Yeah, what about how are people in the community getting to learn form everyone else? When you talk about a platform it should be if I do something not only can by organization learn from it, but potentially others can learn from it. That's kind of the value proposition of SaaS. >> Yes, yes it and having the galaxy offering out there, where we see so many users contributing, like we have close to a hundred thousand rolls out there now and that really brought the Ansible community together. It was already a strong community of contributors and everything. By giving them a platform where they can have these discussions, where they can see what everybody else is doing, it's the story is where you will now see a lot more happening like today, I think it was Ansible is like the top five Get Up projects in terms of progress that are happening out there. I mean the community is so wide run, it's incredible. Like they're driving this change and it's a community made up of developers, a lot of them. And that's what's creating this amazing synergy between all the different organizations. So, we feel that Ansible is actually bringing a lot of us together. Especially, as more and more automation becomes prevalent in the organizations. >> Alright, Parag want to give you a final word, Ansible Fest 2019, final take aways. >> No, this is great, this is my first one and I'd never been to one before and just the energy, and just seeing what all the other partners are also sharing, it's incredible. And Like I said with my backgrounds automations, I love this, anything automation for me, I think that's just the way to go. >> John: Alright, well that's it. >> Stu: Thank you so much for sharing the developer angle with us >> Thank you very much. >> For John Furrier, I'm Stu Miniman. Back to wrap-up from theCUBe's coverage of Ansible Fest 2019. Thanks for watching theCUBE. (intense music)

>> 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)