>> 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)

>> Announcer: Live, from San Jose, in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2017. Brought to you by Hortonworks. (electronic music fading) >> Hi everybody, this is George Gilbert. We're watching theCUBE. We're at DataWorks 2017 with my good friend Sri Raghavan from Teradata, and Sri, let's kick this off. Tell us, bring us up to date with what Teradata's been doing in the era of big data and advanced analytics. >> First of all, George, it's always great to be back with you. I've done this before with you, and it's a pleasure coming back, and I always have fun doing this. So thanks for having me and Teradata on theCUBE. So, a lot of things have been going on at Teradata. As you know, we are the pioneer in the enterprise data warehouse space. We've been so for the past 25 plus years, and, you know, we've got an incredible amount of goodwill in the marketplace with a lot of our key customers and all that. And as you also know, in the last, you know, five or seven years or so, between five and seven years, we've actually expanded our portfolio significantly to go well beyond the enterprise data warehouse into advanced analytics. We've got solutions for the quote-unquote the big data, advanced analytics space. We've acquired organizations which have significant amount of core competence with enormous numbers of years of experience of people who can deliver us solutions and services. So it's fair to say, as an understatement, that we have, we've come a long way in terms of being a very formidable competitor in the marketplace with the kinds of, not only our core enterprise data warehouse solutions, but also advanced analytics solutions, both as products and solutions and services that we have developed over time. >> So I was at the Influencer Summit, not this year but the year before, and the thing, what struck me was you guys articulated very consistently and clearly the solutions that people build with the technology as opposed to just the technology. Let's pick one, like Customer Journey that I remember that was used last year. >> Sri: Right. >> And tell us, sort of, what are the components in it, and, sort of, what are the outcomes you get using it? >> Sure. First of all, thanks for picking on that point because it's a very important point that you mentioned, right? It's not- in today's world, it can't just be about the technology. We just can't go on and articulate things around our technology and the core competence, but we also have to make a very legitimate case for delivering solutions to the business. So, our, in fact, our motto is: Business solutions that are technology-enabled. We have a strong technology underpinning to be able to deliver solutions like Customer Journey. Let me give you a view into what Customer Journey is all about, right? So the idea of the Customer Journey, it's actually pretty straightforward. It's about being able to determine the kind of experience a customer is having as she or he engages with you across the various channels that they do business with you at. So it could be directly they come into the store, it could be online, it could be through snail mail, email, what have you. The point is not to look at Customer Journey as a set of disparate channels through which they interact with you, but to look at it holistically. Across the various areas of encounters they have with you and engagements they have with you, how do you determine what their overall experience is, and, more importantly, once you determine what their overall experience is, how can you have certain kinds of treatments that are very specific to the different parts of the experience and make their experience and engagement even better? >> Okay, so let me jump in for a second there. >> We've seen a lot of marketing automation companies come by and say, you know, or come and go having said over many generations, "We can help you track that." And they all seem to, like, target either ads or email. >> Correct. >> There's like, the touchpoints are constrained. How do you capture a broader, you know, a broader journey? >> Yeah, to me it's not just the touchpoints being constrained, although all the touchpoints are constrained. To me, it's almost as if those touchpoints are looked at very independently, and it's very orthogonal too, right? I look at only my online experience versus a store experience versus something else, right? And the assumption in most cases is that they're all not related. You know, sometimes, I may not come directly to the store, right, but the reason why I'm not coming to the store is because, to buy things, because, you know, I have seen an advertisement somewhere which says, "Look, go online and purchase a product." So whatever the case might be, the point is each part of the journey is very interrelated, and you need to understand this is as well. Now, the question that you asked is, "How do you, for instance, collect all this information? "Where do you store it?" >> George: And how do you relate it ... >> And, exactly, and how do you connect the various points of interaction, right? So for one thing, and let me just, sort of, go a little bit tangential and go into some architecture, the marchitecture, if you will that allows us to be able to, first of all, access all of this data. As you can imagine, the types and the sources of data are quite a bit, are pretty disparate, particularly as the number of channels by which you can engage with me as an organization has expanded, so do the number of sources. So, you know, we have to go to place A, where there's a lot of CRM information for instance, or place B, where it's a lot of online information, weblogs and web servers and what have you, right? So, we have to go to, for instance, some of these guys would have put all this information in a big data lake. Or they could have stored it in an EDW, in an enterprise data warehouse. So we've put in place a technology, an architecture, which allows us to be able to connect to all these various sources, be it Teradata products, or non-Terada- third-party sources, we don't care. We have the capability to connect all to, to these different data sources to be able to access information. So that's number one. Number two is how do you normalize all of this information? So as you can well imagine, right, webs logs servers are very different in their data makeup as apposed to CRM solutions, highly structured information. So we need a way to be able to bring them together, to connect a singular user ID across the different sources, so we have filtering, you know, data filters in place that extracts information from weblogs, let's say it's a XML file. So we extract all that information, and we connect it. We, ultimately, all of that information comes to you in a structured manner. >> And can it, can it be realtime reactive? In other words when- >> Sri: Absolutely. >> someone comes to- >> Sri: Absolutely. >> you know, a channel where you need to anticipate and influence. >> Very good question. In fact, I think we will be doing a big disservice to our customers if we did not have realtime decisioning in place. I mean, the whole idea is for us to be able to provide certain treatments based on what we anticipate your reactions are going to be to certain, let's say if it's a retail store, let's say to certain product coupons we've placed, which says, you know, come online, and basically behavior we think there's a 90% chance that tomorrow morning you're going to come back, you know, through our online portal and buy the products. And because of the fact that our analytics allows us to be able to predict your behavior tomorrow morning, as soon as you land on the online portal, we will be able to provide certain treatment to you that takes advantage of that. Absolutely. >> Techy question: because you're anticipating, does that mean you've done the prediction runs, batch, >> Sri: Absolutely. >> And so you're just serving up the answer. >> Yeah, the business level answer is absolutely. In fact, we have, as part of our advanced analytics solution, we have pre-built algorithms that take all this information that I've talked to you about, where it's connected all that information across the different sources, and we apply algorithms on top of that to be able to deliver predictive models. Now, these models, once they are actually applied as and when the data comes in, you know, you can operationalize them. So the thing to be very clear here, a key part of the Teradata story, is that not only are we in a position to be able to provide the infrastructure which allows you to be able to collect all the information, but we provide the analytic capabilities to be able to connect all of the data across the various sources and at scale, to do the analytics on top of all that disparate data, to deliver the model, and, as an important point, to operationalize that model, and then to connect it back in the feedback loop. We do the whole thing. >> That's, there's a lot to unpack in there, and I called our last guest dense. What I was actually trying to say, we had to unpack a dense answer, so it didn't come out quite that, quite right. So I won't make that mistake. >> Sri: That's a very backhanded compliment there. (George laughing) >> So, explain to me though, the, I know from all the folks who are trying to embed predictive analytics in their solutions, the operationalizing of the model is very difficult, you know, to integrate it with the system of record. >> Yeah, yeah, yeah. >> How do, you know, how do you guys do that? >> So a good point. There are two ways by which we do it. One is we have something called the AppCenter. It's called Teradata AppCenter. The AppCenter is a core capability of some of the work we've done so far, in fact we've had it for the last, I don't know, four years or so. We've actually expanded it across, uh, to include a lot of the apps. So the idea behind the AppCenter is that it's a framework for us to be able to develop very specific apps for us to be able to deliver the model so that next time, as and when realtime data comes in, when you connect to a database for instance. So the way the app works is that you set up the app. There's a code that we've created, it's all prebuilt code that he put behind that app, and it runs, the app runs. Every time the data is refreshed, you can run the app, and it automatically comes up with visualizations which allow you to be able to see what's happening with your customers in realtime. So that's one way to operationalize. In fact, you know, if you come by to our booth, we can show you a demo as to how the AppCenter works. The other say by which we've done it is to develop a software development kit where we actually have created an operationalization. So, as an, I'll give you an example, right? We developed an app, a realtime operationalization app where the folks in the call center are assessing whether you should be given a loan to buy a certain kind of car, a used car, brand new car, what have you the case might be. So what happens is the call center person takes information from you, gets information about, you know, what your income level is, you know, how long you've been working in your existing job, what have you. And those are parameters that are passed into the screen- >> By the way, I should just say, on the income level, it's way too low for my taste. >> Those are, um, those are comments I'll take, uh, later. >> Off slide. >> But, I mean, you got a brand new Armani suit, so you're not doing badly. But, uh, so what happens is, you know, as and when the data goes into the parameters, right, the call center person just clicks on the button, and the model which sits behind the app picks up all the parameters, runs it, and spews out a likelihood score saying that this person is 88% likely- >> So an AppCenter is not just a full end to end app, it also can be a model. >> AppCenter can include the model which can be used to operationalize as and when the data comes in. >> George: Okay. >> It's a very core part of our offering. In fact, AppCenter is, I can't stress how important, I can't stress enough how important it is to our ability to operationalize our various analytic models. >> Okay, one more techy question in terms of how that's supported. Is the AppCenter running on Aster or the models, are they running on Aster, uh, the old Aster database or Teradata? >> Well, just to be clear, right, so the Aster solution is called Aster Analytics of which one foreign factor contains a database, but you have Aster which is in Hadoop, you have Aster in the Cloud, you have Aster software only, so there's a lot of difference between these two, right? So AppCenter sits on Aster, but right now, it's not just the Aster AppCenter. It's called the Teradata AppCenter which sits on, with the idea is that it will sit on Teradata products as well. >> George: Okay. >> So again, it's a really core part of our evolution that we've come up with. We're very proud of it. >> On that note, we have to wrap it up for today, but to be continued. >> Sri: Time flies when you're having fun. >> Yes. So this is George Gilbert. I am with Sri Raghavan from Teradata. We are at DataWorks 2017 in San Jose, and we will be back tomorrow with a whole lineup of exciting new guests. Tune in tomorrow morning. Thanks. (electronic music)