>>Welcome back everyone to the Cube's live coverage of snowflake summit 22, we are live in Las Vegas. Caesar's forum, Lisa Martin, Dave Valante, Dave. This is day one of a lot of wall action on the, >>Yeah. A lot of content on day one. It, it feels like, you know, the, the reinvent fire hose yes. Of announcements feels like a little mini version of that. >>It does. That's a good, that's a good way of putting it. We've been unpacking a lot of the news. That's come out, stick around, lots more coming. We've got two guests joining us from tech systems global services. Please welcome Devon. Pania managing director and Shai Sheva of us senior and Shire. Shrivastava senior manager, guys. Great to have you on the cube. >>Thank you so much. Good to see you. And it's great to be in person. Finally, it's been a long UE, so excited to be here. >>Agree. The keynote this morning was not only standing room only, but there was an overflow area. >>Oh my goodness. We have a hard time getting in and it is unbelievable announcement that we have heard looking forward for an exciting time. Next two days here >>Absolutely exciting. The, the cannon shotgun of announcements this morning was amazing. The innovation that has been happening at snowflake and you know, this clearly as partner has been, it just seems like it's the innovation flywheel is getting faster and faster and faster. Talk to us a little bit, Devon about tech systems. Give us the audience a little bit of an overview of the company, and then talk to us about the partnership with snowflake. >>Sure. Thank you. Lisa tech system global services is a full stack global system integrator working with 8% of fortune 500 customers helping in accelerating their business as well as technology modernization journey. We have been a snowflake partner since 2019, and we are one of the highest accredited sales and technical certification with snowflake. And that's what we have earned as a elite partner or sorry, emerging partner with snowflake last year. And we are one of the top elite partner as well. >>Yeah. So since 2019, I mean, in the keynote this morning, Frank showed it. I think Christian showed it as well in terms of the amount of, of change innovation that's happened since 2019 Ellen, we were talking before we went live to share about the, the last two years, the acceleration of innovation cloud adoption digital transformation. The last two years is kind of knock your head back. You need a yeah. A whiplash collar to deal with that. Talk about what you've seen in the last three years, particularly with the partnership and how quickly they are moving and listening to their customers. >>Yeah. Yeah. I think last two years really has given pretty much every organization, including us and our customers a complete different perspective. And that's, that's the exact thing which Christian was talking about, you know, disruption, that's the that's that has been the core message, which we have seen and we've got it from the customers. And we have worked on that right from the get go. We have, you know, all our tools and technology. We are working hand in hand with snowflake in terms of our offerings, working with customers, we have tools. We talk about, you know, accelerators quote unquote that's that helps our customers, you know, to take it from on-prem systems to all the way to the snowflake data cloud and that too, you know, fraction of seconds. You talk about data, you talk about, you know, code conversion, you talk about data validation. So, you know, there are ample amount of things, you know, in terms of, you know, innovation, all workload, I've heard, you know, those are the buzzwords today, and those are like such an exciting time out here. >>So before the pandemic, you know, digital transformation, it was, it was sort of a thing, but it was, it was also a lot of complacency around it. And then of course, if you weren't in a digital business, you were out of the business and boom. So you talked to bang about the stack. You guys obviously do a lot in cloud migration. What's changed in cloud migration. And how is the stack evolving to accommodate that? >>That's a great question there when last two years, it's absolutely a game changer in terms of the digital transformation. Can we believe that 90% of world's data that we have produced and captured is in last two years? It's, isn't that amazing? Right. And what IDC is predicting by 20 25, 200 terabytes of data is going to be generated. And most of them is going to be unstructured. And what we are fascinated about is only 0.5% of unstructured data is currently analyzed by the organization to look at the immense opportunity in front of us and with Snowflake's data cloud, as well as some of the retail data cloud finance and healthcare data cloud launching, it's going to immensely help in processing that unstructured data and really bring life to the data in making organization and market leader. >>Quick, quick fall, if I could, why is, is such a small, why is so much data dark and not accessible to organizations? What's >>The, that's a, that's a great question. I think it's a legacy that we have been trained such a way that data has to be structured. It needs to be modeled, but last decade or so we have seen note it hasn't required that way. And all the social media data being generated, how we communicate in a world is all arm structure, right? We don't create structured data and put it into the CSV and things like that. It's just a natural human behavior. And I think that's where we see a lot of potential in mining that dataset and bringing, you know, AI ML capabilities from descriptive to diagnostic analysis, moving forward with prescriptive and predictive analytics. And that's what we heard from snowflake in Christian announce, Hey, machine learning workload is going to be the key lot of investment happening last 10 years. Now it's going to, you know, capitalize on those ROI in making quick decisions. >>Should you talk to me about those customer conversations? Obviously they have they've transformed and evolved considerably. Yeah. But for customers that have this tremendous amount of unstructured data, a lot of potential as you talked about dung, but there's gotta be, it's gotta be a daunting task. Oh yeah. But these days, every company has to be a data company to be successful, to be competitive and to deliver the experience that the demanding consumers expect. Yeah. How do you start with customers? Where do they start? What's that conversation like and how can tech systems help them get rid of that kind of that daunting iceberg, if you will and get around >>It. Yeah, yeah, yeah, exactly. And I think you got the right point there. Unstructured data is just the tip of the iceberg we are talking about and we have just scratched little surface of it, you know, it's it's and as the one was mentioning earlier, it's, it's gone out those days, you know, where we are talking about, you know, gigabytes of data or, you know, terabytes. Now we are talking about petabytes and Zab bytes of data, and there are so many, and that's, that's the data insight we are looking for and what else, you know, what best platform you can get better than, you know, snowflake data cloud. You have everything in there. You talk about programmability today. You know, Christian was talking about snow park, you know, that, that gives you all the cutting edge languages. You talk about Java, you talk about scale, you talk about Python, you know, all those languages. >>I mean, there were days when these languages, you need to bring that data to a separate platform, process it and then connect it. Now it is right there. You can connect it and just process it. So I think that's, that's the beginning. And to start the conversation, we always, you know, go ahead and talk to the customers and, you know, understand their perspective, know where they want to start, you know, what are their pain points and where they, they want to go, you know, what's their end goal, you know, how they want to pro proceed, you know, how they want to mature in terms of, you know, data agility and flexibility and you know, how do they want to offer their customers? So that's, that's the basically, you know, that's our, the path forward and that's how we see it. >>And just, >>Just to add on top of that, Dave, sorry about that. What we have seen with our customers, the legacy mindset of creating the data silos, primarily because it's not that they wanted it that way, but there were limitations in terms of either the infrastructure or the unlimited scalability and flexibility and accent extensibility, right? That's why those kind of, you know, work around has been built. But with snowflake unified data cloud platform, you have everything in unified platform and what we are telling our customers, we need to eliminate the Datalog. Yes, data is a new oil, but we need to make sure that you eliminate the Datalog within the enterprise, as well as outside the enterprise to really combine then and get a, you know, valuable insight to be the market leader. >>You know, when the cube started, it was 2010. And I remember we went to Hadoop world and it was a lot of excitement around big data and yes, and it turned out, it didn't quite live up to the expectations. That's an understatement, but we, we learned a lot and we made some strides and, and now we're sort of entering this, this new era, but you know, the, the, the last era was largely this big batch job right now, today. You're seeing real time, you know, we've, we've projected out real time in, is gonna become more and more of a thing. How do you guys see the, the sort of data patterns changing and again, where do you see snowflake fitting in? >>Yeah. Great question. And they, what I would have to say, just in a one word is removing the complexity and moving towards the simplicity. Why the legacy solutions such as big data didn't really work out well, it had all the capabilities, but it was a complex environment. You need to really be, you know, knowing a lot of technical aspect of it. And your data analyst were struggling with that kind of a tool set. So with snowflake simplicity, you can bring citizen data scientists, you can bring your data scientists, you can bring your data analysts, all of them under one platform, and they can all mine the data because it's all sitting in the one environment, are >>You seeing organizations change the way they architect their data teams? And specifically, are you seeing a decentralization of data teams or you see, you mentioned citizen data scientists, are you seeing lines of business take more ownership of the data or is it still cuz again, that big data era created this data science role, the data engineering role, the data pipeline, and it was sort of an extension of the sort of EDW. We had a, a few people, maybe one or two experts who knew how to use the system and you build cubes. And it was sort of a, you know, in order of magnitude more complex than that could maybe do more, but are you seeing it being pushed out to the lines of business? >>That's a great question. And I think what we are seeing in the organization today is this time is absolutely both it and business coming together, hand in hand. It's not that, Hey, it, you do this data pipeline work. And then I will analyze this data. And then we'll, you know, share the dashboards to the CEO. We are seeing more and more cohesiveness within the organization in making a path forward in making the decision intelligence very, very rapid. So I think that's a great change. We don't need to operate in silos. I think it's coming together. And I think it's going to create a win-win combination for our >>Customers. Just to add one more point, what the one has mentioned. I think it's the world of data democratization we are talking about, you know, data is available there, insights. We need to pull it out and you know, just give it to every consumer of the organization and they're ready to consume it. They are, they are hungry. They are ready to take it. You know, that's, that's, that's something, you know, we need to look forward for. >>Well, absolutely look forward to it. And as you talked about, there's so much potential it's we see the tip of the iceberg, right? There's so much underneath that guys. I wish we had more time to continue unpacking this, but thank you so much for joining Dave and me on the program, talking about tech systems and snowflake, what you guys are doing together and what you're enabling those end customers to achieve. We appreciate your insights. >>Yeah. Thank you so much. It's an exciting time for us. And we have been, you know, partnering with snowflake on retail data cloud launch, as well as some upcoming opportunity with manufacturing and also the financial competency that we have earned. So I think it's a great time for us ahead in future. So >>Excellent. Lots to come from Texas systems guys. Thank you. We appreciate your time. Thank you. >>Appreciate it. Thank you. Let it snow. I would say let >>It snow, snow. Let it snow. I like that. You're heard of your life from hot Las Vegas for our guests and Dave ante. I'm Lisa Martin. We are live in Las Vegas. It's not snowing. It's very hot here. We're at the snowflake summit, 22 covering that stick around Dave and I will be joined where next guests in just a moment.

>>Live from San Diego, California at the cue covering to clock in cloud native con brought to you by red hat, the cloud native computing foundation and its ecosystem Marsh. >>Welcome back to the cube here in San Diego for cube con cloud native con 2019. It's our fourth year of doing the cube here. I'm Stu Miniman. It's my fourth time I've done this show. Joining me is Justin Warren. He's actually been to more of the coupons than the cube has, I think at least in North America. And welcome into the program to two veterans of these events from pure storage. Uh, sitting to my right is she'll be uh, Shrivastava who's a director of product marketing and sitting to her right is Anthony lay Ferrario who's a senior product manager, uh, both of you with pure storage. Thank you so much for joining us. Thanks for having us. All right, so, so we, we were kind of joking about veterans here because we know that things are moving faster and faster. You both work for storage companies. Storage is not known to be the fastest moving industry. Um, it's been fascinating for me to watch kind of things picking up the pace of change, especially when you talk about, uh, you know, how developers and you know, software and a multicloud environment, a fit-out. So she'll be maybe, you know, give us a frame for, you know, you, you know, you're in a Cooper ladies tee shirt here pures at the show. How should we be thinking about pure in this ecosystem? >>Sure. Yeah. So, uh, you're, as, you know, we, we side off as all flash on brand storage company, uh, 10 years ago and, uh, we've kept pace with constantly innovating and making sure we're meeting our customer's needs. One of the areas of course that we see a lot of enterprises moving today is two words, microservices, two words, containerized applications. And our goal that you're really is to help customers modernize, modernize their applications while still keeping that store it's seamless and keeping that, uh, invisible to the application developers. >> I think it actually lines up really well if you're do just a pure sort of steam across time has been performance with simplicity. Right? And I think the simplicity argument starts to mean something different over time, but it's a place that we still want to really focus as our customers started to use, uh, try to containerize our applications. >>There are couple of challenges. We saw continued environments, of course, they're known for their, uh, agility, uh, how portable they are. They're lightweight and they're fast. And when they're fast, storage can sometimes be a bottleneck because your storage might not necessarily scale as fast. It might not be able to provision storage volumes as fast, your container environment. And that's the challenge that we at pure why to solve with our Cuban eighties integrations. Anthony, you mentioned simplicity there. So I'm going to challenge you a bit on that because Kubernetes is generally not perceived as being particularly simple and the storage interfaces as well, like stateful sets is kind of only really stabilized over the last 18 months. So how >>is pure actually helping to make the Cuban Eddie's experience simpler for developers? Yeah, and you know, you're totally right. I don't think I was necessarily saying that someone looking for the simplest thing that could ever find would adopt Kubernetes and expect to find that. But what I really meant was, you know, on one hand you have, you know, your more traditional enterprise infrastructure type folks who are trying to build out the underlying private cloud that you're going to deploy, you know, your infrastructure on. And on the other hand, you have your developers, you have your Kubernetes, you have your cloud native applications, right? And really the interface between those is where I'm looking at that simplicity argument because traditionally pure has focused on that simple interface to the end user. But the end user, as we were talking about before, the show has shifted from a person to being a machine, right? >>And the objective for pure and what we're building on the cognitive side is how do we take that simple sort of as a service consumption experience and present that on top of what looks like a traditional infrastructure platform. So I can get more into the, the details of that if you'd like, but really that that layer is where we're focused on the simplicity and really just asking the, the, uh, the end user as few questions as we can. Right. I just want to ask you, what do you need? I don't want to ask you, well, tell me about the, you know, IQN and blah, blah. They don't want that, right? That's the simplicity I'm talking about. Yeah. Well, you run developers generally, I mean, the idea of dev ops and I challenge people whenever they mentioned dev ops, and I'm hearing a pretty consistent message that developers really don't care about infrastructure and don't want to have anything to do with it at all. >>So if you can just bake it into the system and somehow make it easier to operate it, that kind of SRE level, that infrastructure level that, that Kubernetes as a platform. So once that's solved, then as a developer, I can just get on with, with writing some code. We definitely want stories to be invisible. Yeah. So if you want, but if they want stories to be invisible, that's not so great for your brand because you actually want them to know and care about having a particular storage platform. So how do you, how do you balance that idea that we want to show you that we can have to have innovative products that you care about the storage, but you also don't need to care about the storage at all because we'll make it invisible. How does that work? >>So Coupa storage for container environments has been a challenge. And what we are trying to educate the platform level users is that with the right kind of storage, it can actually be easy stores. For QA, these can be easy. And, uh, the way we make it simple or invisible is through the automation that we provide. So pure service orchestrator is our, uh, automation for storage delivery into the containerized environments. And so it's delivered to a CSI plugin, but we tried to do a little more than just develop a plugin into your Cubanetis environments. We tried to make your scalability seamless, so it's super easy to add new storage. And, um, so yeah, I think because a container environments were initially developed for States, less applications when became to staple applications, they still think about, Oh, why should I care about storage? But people are slowly realizing that we need care about it because we don't want to ultimately be bothered by it. Right. >>And if I can make, if I can make a point to just tag on to that I, the conversations I've had at the show this week, I've even helped me sort of crystallize the way I like to explain this to people, which is at first, you know, a lot of people will say, Oh, I don't, I don't do stateful application. I'm doing stateless applications and competitors. And my response is, okay, I understand that you've decided to externalize the state of your system from your Kubernetes deployment. But at some point you have to deal with state. Now, whether that's an Oracle database, you happen to be calling out to outside of your community's cluster, whether that's a service from a public cloud like S3 or whether that's deciding to internalize that state into Coobernetti's and manage it through the same management plane you have to have state. >>Now when we talk about, you know, what we're doing in PSO and why that's valuable and why, you know, to your point about the brand, I don't necessarily worry is because when we can give a seamless experience at the developer layer and we can give the SRE or the cluster manager layer a way where they can have a trusted high performance, high availability storage platform that their developers consume without knowing or worrying about it. And then as we look into the future, how do we handle cross cluster and multi-cloud stateful workloads, we can really add value there. >>Well, yeah, and I'm glad you brought up the multi-cloud piece of it because one of the more interesting things I saw from pure this year is how pure is putting in software cloud native. Um, so when I saw that one of the questions like, okay, when I come to a show like this, how does Kubernetes and containers fit into that old discussion? So how help us connect the dots as to what was announced and everything else that's happening. >>You've heard about cloud block store, which is our software running on the AWS cloud today. And uh, that's basically what we've done is we've people have loved flash array all these years for the simplicity it provides for the automation and performance. You want to give you something similar and something enterprise grade in the public cloud. The cloud, Luxor is basically, you can think of it as a virtual flash array and on the AWS cloud. So with that, you now have D duplication, 10 provisioning capabilities in the cloud. You can, um, be brought an active cluster, which is active, active, synchronous replication between availability zones. So really making your AWS environments ready for mission critical applications. Plus with our, you know, PSO just works the same way on prem as in the cloud. So it's just great for hybrid application mobility. You have the same APIs. >>Yeah, it's actually very cool. Right? One of the, one of the, you know, fun things for me as a software developer at pure, at a software side guy at pure, um, is that the API's that our arrays have are the same API. It's actually the same underlying software version even though it's a totally different hardware, hardware back end implementation. When we run in a cloud native form factor versus when we run in a physical appliance form factor, the replication engines work between the two snapshots, clones. Um, our ability to do instant, um, restores like everything that we do and that has brought value from our, our storage software stack, we still get access to in a cloud native environment and the transports as well. I guess trying to understand, is there Kubernetes involved here or is this just natively in AWS? And then then on premises itself is a, >> is a compute orchestration layer component. So when I look at Kubernetes, I'd say Kubernetes sits above both sides, right? Or potentially above and across both sides, um, depending on how you decide to structure your environment. But the nice part is if you've developed a cloud native application, right, and that's running on Kubernetes, the ability to support that with the same storage interfaces, the same SLS, move it efficiently, copy it efficiently and do that on whatever cloud you care to do. That's where it gets really cool. >>So we developed this really cool demo where you have a container application running on PSO, on flash array, on prem. We migrated that to cloud block store and on AWS and it just runs, you use the same yanno scripts in both places. There is no need to, you know, do a massive rearchitecture anything. Your application just runs when you move it. And we take care of all the data mobility with our asynchronous replications, you can take a snapshot on prem, you can snap it out into AWS, restore it back into cloud block store. So it really opens up a lot of new use cases and make them simple for customers >>that that idea of write once run anywhere. I said I'm, I'm old enough to remember when Java was a brand new thing and that was the promise. And it never quite got there because it turns out it's really, really hard to do that. Um, but we are seeing for from pure and from a lot of vendors here at the show that there's a lot of work and effort being put in into that difficult problem so that other people don't have to care about it. So you're building that abstraction in and, and working on how this particular, how the details of this work. And, uh, I was fortunate enough to get a deep dive into the end of the architecture of cloud Brock's door, just a recent accelerate conference and the way you've actually used cloud resources as if they were kind of infrastructure components and then built the abstraction on top of it, but in the same way that it runs on site, it, that's what gives you that ability to, to keep everything the same and make it simple, is doing a lot of hard work and hard engineering underneath so that no one has to care anymore. >>Yeah. And the way we've architected CloudLock store is that, you know, be as use the highest performance performing, uh, AWS infrastructure. And the highest durability it this infrastructure. So you're actually now able to buy performance and, and durability in one through one single virtual appliance as you would. >>Yeah. How's the adoption of the products going? I know it was, it was very early when it was announced just a few months ago. So what's the feedback from customers been so far about? >>It's been really positive and actually, you know, the one use case that I want to highlight really most is actually dev ops use cases, right? This, the value add of being able to have the same deployment for that application for a test or dev infrastructure in one cloud versus a production to point them in another cloud has been very exciting for folks. So, you know, when you think about that use case in particular, right? The ability to say, okay, I'm coming up to a major quarterly release or whatever I have for my product, I need to establish a bunch more test environments. I don't necessarily want to have bought that and we're not necessarily talking about, you know, bursting over the wire anymore. Right. We're talking about local, uh, local storage under the same interfaces in the cloud that you choose to spin up all of those test environments. So cases like that are pretty interesting for folks. >>Yeah. I think that's how people have started to realize that it's that operation side of things. It's not even day to day 90 and day 147 where I want to be operating this in the same place in the same way no matter where it is because it just saves me so much heartache and time of not having to re implement differently and I don't have to retrain my resources because it all looks the same. So, uh, yeah, Def does definitely have a big use case migration through verbose. That's another use case that we are seeing a lot of customers interested in and uh, disaster recovery, using it as a disaster recovery. How do you, so you can efficiently store backups on Amazon S three, but how do you do an easy fast restore to actually run your applications there? So with CloudLock store, it is now possible to do that, to do a fast, easy restore. Also a couple of weeks ago actually, we started taking registrations for a beta program for cloud Glocks or for Azure as well. Uh, yup. Customers are going multi-cloud. We are going multi-cloud with them. >>Great. I want to give you both a final word, uh, takeaways for a pure storage participation here at the show. >>I think the biggest thing that I, that I want people to understand, and I actually gave this talk at the cloud native storage day on day zero is that cloud native storage is an approach to storage. There's not a location for storage. And I think pure storage that really defines to me the way we're going about this, we're trying to be cloud native storage wherever you need it. So that's, that's really the takeaway I'd like people to have about pure >>and cute and storage for Cuban. It is, doesn't have to be hard. We are here all day today as well. So, um, I mean this is a challenge the industry seeing today and uh, we have a solution to solve that for you. >>All right, well that's a, that's a bold statement, uh, to help end us as Shilpi. Anthony, thank you so much for joining us for Justin Warren. I'm Stu Miniman back with more coverage here from cube con cloud native con 2019 stay classy, San Diego. And thanks for watching the queue.

>> 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)