>> 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)