>> Announcer: Live from Las Vegas, it's theCUBE! Covering Informatica World 2018. Brought to you by Informatica. >> Hey welcome back, everyone. Live here in Las Vegas, this is theCUBE's coverage of Informatica World 2018. I'm John Furrier co-host of theCUBE with Peter Burris, my co-host for the next two days. Chris Wojdak who's the Production Architect at Symcor, a Canadian leading financial processing services provider. Welcome to theCUBE. >> Thank you, great to be here, guys. >> So first explain, about in one minute, what the company does and your role. >> Yeah, so Symcor was formed by the three largest banks in Canada, over 20 years ago. We have a proven ability to work effectively as a utility service structure type of model. Symcor is a leading business processing and client communications provider in Canada, supporting banks, telecommunications, insurance, and retail companies in Canada. >> John: And your role there is to do what? Deployment of data, deployment? Be specific. >> Yeah, specifics, one of the things that I work on is strategic initiatives. Everything from data-driven architectures to the strategies, where we want to take the company and how do we, how does the technology line up to the business needs. Such that I'm a Senior Architect in the office as a CTO. >> So what's your data look like? I mean, obviously, you're an Informatica customer. >> Are you happy with Informatica? And are they helping you out? And what's the, tell us about, tell us what's going on. >> Anybody who knows me will know that I'm a pretty blunt guy, so when I say this, I do mean it is, Informatica has done tremendous things for us. Their products actually just work. It's very easy to get value out of our data using Informatica. Our time to market has decreased from months to weeks with them. So we're extremely happy with the maturity of their products and services that we get from them. >> So as you think about the role that, that the architecture's played, and you being a, a good example of that. The architect used to be the individual that would look at the physical assets, and how you thought about the physical assets should be put together in response to a known process, >> Chris: Correct. >> and a known application. And now, as you mention, a data-first orientation requires thinking about the arrangement of assets that have to be architected around very differently. >> Absolutely. >> How has the role of architecture changed? Certainly where you are, but in response to this notion of data first. >> Yeah, so one of the biggest challenges that we have is how do we ethically use that data for fraud prevention and detection purposes 'cause that's one of the key areas that we're trying to grow as one of our key initiatives, which is digital and data services. And where we struggle with that is how do we effectively use our data? So we work with our internal teams, like our privacy and data governance teams to come up with a data governance policy, a comprehensive one at that. How do we ethically use this data now for our services? That's the biggest thing that's changed as opposed to just taking our process and gluing it together. How can you use that without breaking laws and things like that? That's the biggest change I see. >> And what's the relationship between architecture, data architecture, or architecture generally and the role that security's playing? We have a feeling that because data can be shared, because it can be copied, 'cause it can be moved, privatizing that data is essential to any business strategy and security historically has played a major role in thinking about how we privatize data. How does security fit into that governance, ethical kind of model? >> Yeah, and we are a security first type of company over anything else a lot of times. They definitely have a seat at the table. We've had to deploy certain things, I'm not sure if you heard of format preserving encryption architectures and techniques to help enable not only to satisfy the governance, but to drive value legally to our businesses, and our clients. >> How do you look at data as a platform, and how is your data laid out? You made a comment earlier which I liked, which was, Informatica products just works. We've been covering them for a few years. One of the things that got my attention was horizontally scaling the data across systems, not just a point product, >> Chris: Exactly. >> more of a platform. How, from your standpoint, do you look at platforms for you? As you re-platform with data, you are digitizing a lot of services, you're actually enabling new services. What is it about the data platform, and how are you guys thinking about it? >> Well, when we're thinking about it, how do we manage data in a centralized spot, and deliver microservices on top of that data in one spot? How do we, because we can't afford to have data in a million data warehouses, or sporadically throughout the organization, it's not an effective use of data. So the way we've tried to structure it is as soon as we get the data in, we keep it in one spot, which in our case would be the Tera Hadoop cluster. Fully encrypted using format preserving encryption as our mechanism to securing the data. And then from there, running microservices on top of our Hadoop stack power byte Informatica, to drive value out of that data. And where the biggest bang for our buck a lot of times is is that, mainly we have old mainframe data file structured data that's hard to parse and deal with. Well, we can store it in Hadoop, save the space, 'cause it's highly compressed, like X9 or EBCDIC, use Informatica to just get at it in a matter of minutes, to drive value in weeks versus months in a traditional model >> Talk about the microservices architecture because that's kind of a methodology, kind of a mindset. Is it like the classic cloud, Kubernetes containers, or you think of it more of endpoint APIs, talk about how you define microservices. >> Yeah, so microservices, where we've leveraged microservices is essentially in our in our new development models where we're utilizing node.js, and react, single page application development, where we have this in the front end just talking to microservice, specifically, delivering on a specific need only. And then we're leveraging things like, for instance, Kubernetes in the backend, where we deploy those microservices, but we're dealing with it from a single page application perspective, really the more modern web development approach is. >> So you're bringin' data into the application, via microservices, so you can have the centralized location, microservices handles the interaction, and it inputs that into the application? >> Right, and then which also, we have to rework the security infrastructure, and approach to it, because we couldn't use the old school, let's see, Jade session, cookie, now we're using token-based authentication, and all these challenges there, right? >> Hey, I love it, we're at a data show, and we're talkin' Kubernetes, and orchestration containers, and microservices, and it's awesome. (laughing) (Chris laughing) >> But that's what those, that's what those technologies are deployed for, right? >> I know, I'm just saying, it's great! >> But I want to push you on this. >> Chris: Yeah, sure. >> So, today, Symcor provides, as you said, a, this enormous facility for looking up past banking transactions or past banking statements for a variety of different banks in Canada. But, I presume you're looking at providing new services in the future. I can imagine that a centralized resource for a human being looking up an old banking statement, well you got, four, five seconds to get the job done, it's probably pretty good. But when you start talking about, maybe moving to fraud detection, or some other types of services, does that start to change the way you think about your data architecture? 'Cause now you're doing something that's much more close to real-time, how's that going to effect the way you think about things? >> Oh, it was a, we've been on a journey, right? On a digital data transformation journey, literally at Symcor because of that. We started off with some in-house built solutions that we have actually patents on, on how to properly warehouse data. We have one of the largest Canada data warehouses for check images, like a 2.6 petabytes in Canada, and we have to somehow, how do we drive value out of this as a data warehouse type of mentality solution, how do we drive value? So how do we move now into more of the Hadoop, the Cassandra's world, to get that real-time batch processing and get insights, and how do we do that ethically as well, right? And secure, how do we secure? Those are the three biggest things that we have to look at in our journey to get there, hasn't been easy, 'cause different paradigms, different understandings-- >> So let me make sure I got that, new technologies to reduce the response times, ethical use of the data, >> The data. >> and secure control in reference to the data? >> Correct, to protect it, yes. >> So how is that changing then, how you think, do you see it staying centralized, do you see it becoming, moving some of the data, some of the responses out closer to some of your banks, who are actually doing the fraud detection? >> Well, we see it, 'cause we're trying to get into this space, and do it on their behalf because, we have that overarching kind of look at this, so how do we just do it ethically, right? So, when some of our owner banks, for example, send this data, well we can provide services overarching to provide insights across the board, something they can not let's say, do on their own, without our help, type of thing. >> Real quick, define data ethics, 'cause you mentioned ethics many times. Do you mean securely, anonymized, what does that mean for you? >> Well, to me it means like that old, you know, 20 years ago for example, I would take my wallet, maybe put it in my vault, in my vault at home, physically protected, it's safe. Well how do I protect that data now, not only from potentially breaches, but how do I protect to make sure my privacy isn't at risk, that someone's not using it for, for improper use, things like that, that's how define ethical use, right? >> What're you doin' now that you couldn't do before, we're seeing this awesome cloud, you mentioned, Kubernetes gets me pumped up, because that's kind of a horizontal orchestration, you talk about multi-cloud, these are things that are, coming into sight with those kinds of technologies. There's an old way, there's a new way, right? (laughs) So we're seeing this transformation, what's different now for you, that you couldn't do before? >> Yeah, before it was hard to drive insights, because we didn't have the scalability horizontally, or vertically, so things like Hadoop, Informatica and Hadoop the way we can scale our web applications with microservices that's what's made the big difference, is the techniques that are being developed to get down to real-time processing, get the answer quicker and faster, and drive value to our clients faster. What's really important is, when they moved to digital channels, you know, fraud becomes a problem it's growing, in incidents and complexity. We see an opportunity now, where we can provide this fraud detection and prevention services as they change and go to digital channels, were there for the ride, type of thing. >> Chris, it's a great interview, I'd love to follow up with you and learn more about your environment. Final question, I heard you got the Informatica innovation award honoring, congratulations! >> Thank you. >> Advice to other folks doing cutting edge stuff that might be interested in in that kind of status? >> Yeah, words of advice there would be, try to push the limits. Never give up, try to push the limits on the design patterns and design approaches. You'd be amazed at what you can achieve if you really push those limits. >> Great story, love what you guys are doing out of Canada, Toronto area, Chris thanks for comin' on theCUBE, appreciate your stories. theCUBE live coverage here in Las Vegas for Informatica World 2018, I'm John Furrier, Peter Burris, we'll be back after this short break. (bubbly music)

>> Announcer: Live from San Francisco, it's the Cube. Covering Informatica World 2017, brought to you by Informatica. >> Okay, welcome back, everyone. We're here live in San Francisco for Informatica World 2017. This is the Cube, a flagship program. We go out to the events and extract the signal from the noise. I'm John Furrier, the host of the Cube. Our next guess is Lima Rice, who's the chief data officer Global Enterprise Data Analytics for Allegis Group. Salema, welcome to the Cube. >> Thank you. >> So you won the Informatica Innovation Award Monday night. Congratulations. >> Salema: Thank you. >> You're also a chief data officer, so we love to have conversations with because really, what does that mean? (laughing) You're the chief of everything now. Data's at the center, you're like the heart surgeon of the organization. What was the award for? Tell us a little bit about Allegis Group. >> Sure. So the award was really about how we're innovating with data. So for us, it's really about using data as an asset and how we really want to transform our company the way that we transformed the industry almost 30 years ago. So we've really partnered with Informatica to build out our Master Data Management, data as a service, data quality, everything that would help us to find the right person for the right position at the right time. >> Talk about what you guys do at Allegis Group and how it fits into your parent company. A lot of folks, you guys are very large, but tell us a little bit about your firm. >> Sure. So Allegis Group is the largest talent solution company in the world, largest privately held one. We have almost 450,000 contract employees worldwide. We have over 500 offices in 53 countries that we service. Our two flagship companies are Tech Systems and Aerotech, which make up probably a little over 75% of our organization. >> So you guys match talent that's in your network with opportunities that they are fit for. >> That's correct, for most of the operating. For Tech Systems and Aerotech, which makes up that large portion, that's exactly what they do. Then we have service organizations and we have managed service programs and vendor management solutions and other operating companies that service other types of industries. >> You mentioned as we were getting started that you use data as a competitive strategy weapon, if you will, key asset. >> Absolutely. >> I kind of weaved in competitive strategy. If you're doing well, it's also competitive strategy. >> Right. >> If you're successful. That's the key conversation here at Informatica World this year and in the industry worldwide as they look at the assets differently. It's not just an accounting thing. Most CFOs know where the plant and equipment are, the depreciation schedule, itemization schedule, all that stuff is kind of like in Financial 101. Data now is coming in as an asset where sometimes they don't even know where the data is, that's one problem. How are you looking at that? Because take us through what that means to make data as the asset, and how do you wrangle it in, how do you get your hands on it so to speak, metaphorically speaking, and then, also, how do you deploy it as an asset? How does it get paid back to the company? >> So that's a lot of questions in there. I'll start with, we believe that being able to ingest any data from anywhere in any format and use that in order to enable our producers which are our recruiters and our account managers to make better, faster decisions and really reduce our risk is a way that we can help produce and make quality, fact-based decisioning. So it all starts with great quality data. When you think about the journey that somebody goes through of getting a job, there's probably not, maybe two other times in your life that are more traumatic, right? So, birth, death, and you know? >> John: I know. >> Getting married, maybe? >> John: That too. >> And changing careers, right? So we try to use data and we try to make the best out of each situation so that people feel like they're really becoming part of our Allegis family and not just taking a job. >> Or a piece, a resume that's on file so to speak. >> Salema: That's right. >> Take us through an example of a use case that someone could relate to with you guys or applying data and the benefit to you guys and your customer. >> So at any given time, we have roughly 55 million resumes that we're parsing through and trying to identify and make the perfect search and match for our customers. And that's really the core part of our business. >> 55 million. >> 55 million resumes. (chuckling) So within that search and match process, it's really important that my team help enable that search and match team with good quality data so that, when you think about, if you have bad data, you're going to make bad decision matching rules. And so the better quality the data, the better we can help that team. >> I mean, everyone's had an experience where they've gotten an email or something where you can see some sort of form was inserted Dear Placeholder, my name, they didn't insert my name. That's just a random example, but that's the kind of example where it's not personalized. It's not a fit for me. I'm like, hey, I'm a machine, you're talking to me, I'm a person, I instantly delete it if it's not already in my spam folder. >> Right. >> Similarly with your, it's a high touch and again, it's intimate-- >> Very much so. >> Very intimate to the user. How will you guys doing that personalization and what's the data angle on that? >> That's very important to us, actually. So when our founders created the company almost 30 years ago, they made three promises. They made a promise to the customer that they would work harder than any other vendor ever worked for them. They wouldn't stop until they filled that rack. To the consultants coming in, they made a promise that they'd never just sling their resumes. That they would get to know them intimately. They would find out their likes, their dislikes, what are things that they want to do to make a life? And then to the people working in-house, they promised that if you would work harder than you ever thought was possible, the company would pour into them and those three things are still the core value of what we do today. So while our competition looks very different today than it used to, I mean, for probably 20 years our competition looked exactly like us. The same model, the same comp model, everything. Until about four years ago, and we started seeing competition that had no brick and mortar, that has no recruiters. We have 25,000 recruiters, we have 500 offices. >> Where was the competition going? All online automated? >> They're going algorithm, so they're going bionic recruitment. The thing for us is that that relationship is what really sets us apart. The relationship means that much to us that we want to use data to enable our recruiters and enable our producers so that they can become more talented advisors and career coaches. >> You know, there's two things that jump in my head. One is, you don't want to be a slave to the algorithm. >> That's right. >> Or slave to process, you want the process to work for you. >> Absolutely. >> The second one is, we always talk about the start-up community and growing companies is that you always hear people, "Oh, he and she is a good fit." You know, being a good fit for a job really is key because you could be in a job and be unhappy and no one wins. >> That's right. >> So getting the fit is critical. So you guys are using humans with machines-- >> That's right. >> Together so you're making the data work for the human process which is a hybrid. >> That's right. We look at it as we use data to have a competitive advantage by empowering our producers and really using that combination of human touch and technology to deliver the best customer experience. >> Okay, talk about the marketplace. As you look back, and you notice your Informatica customer, we'll get to that in a second, but there's a lot of solutions out there. People are peddling software. You got to be kind of a skeptic, but you don't want to miss the wave. >> Salema: That's right. >> The data wave, that that's something you obviously as a Chief Data Officer. So you got to squint through the BS of the fog or the smokescreens that are out there. How do you tell, well first of all, what is the current landscape from your perspective? What's the right solutions that you see emerging out of this new modern era of data at the center? With software, with algorithms, and obviously mixed with humans. What's the big industry trend that you like, and what don't you like? >> Yeah, I love what Informatica's doing. I love that they're combining the best of artificial intelligence and machine learning into every application that they create. That's really critical to us, and I think to every company is we always say as we're teaching our children, if you learn from your own mistakes, you'll be smart. But if you can learn from the mistakes of others, you're going to be a genius. Well, when we make mistakes, if our applications can learn from them, but what if those applications can learn from all the customers and from the information that they're putting in? So Informatica embedding AI with Claire now, I think is genius. I think that it's going to set them apart and really set their customers apart. So that's why we like partnering with them. >> You mentioned data quality. It's one of my favorite topics, and I always talk about dirty data, it's bad for you. Clean data, good data, is really instrumental. >> Salema: That's right. >> How are you guys refreshing the data? Someone from Informatica was on, talking about heartbeat of data as the, but also that implies the heart is a critical organ so you need a surgeon for that, heart surgery. But sometimes, data hygiene. You need a data hygienist. So there's a spectrum of data interaction points. What's your thoughts on data quality? What are the key things you keep on top of to keep the data high quality? >> It's really important to us. We use, so if you think about one of the things that makes a great match for somebody, it's about the proximity to your position. So making sure that the addresses are clean. We use Informatica's data as a service. And we do all world geo lat long, and we do Address Doctor and address verification. Email verification is big in our business. Phone number cleansing, and then just overall making sure that we have a single golden record. If you think about somebody like me, I started with the company in 1998 as a consultant. So being out there as a consultant for 23 years and then coming in-house, all of my data from my maiden name still exists in our systems. So really, it's about not just cleansing good to bad, but making sure that you're creating that golden record of a person so somebody on LinkedIn might just put their first initial or on any third party system and knowing that those are all still the same person and making sure that we're connecting the right people is really important to us. >> You bring up such good points I don't even think about. Most people don't think about. But one of the most satisfying things about a job is the commute. I live in the Bay Area here. (laughing) If I'm in East Bay and I've got to go to Palo Alto, that's a nightmare. But that depends on the opportunity, right? So that's a blend. And the other one is the role of new data. So you mentioned LinkedIn. So LinkedIn seems to be a contextual resume, and in short term social network, which they're doing a decent job with. But that's more data. Reputation's super important in the world you're in. >> That's right, right. >> How are you guys looking at that? Because I can see how you guys got the blend of machines and humans, that's nice. Business philosophy's awesome. How do you guys get more reputation data points, too? Look for those blind spots. >> Sure. Well one of the things we do is by taking the person's information. One of the things that I think sets us apart from our competition is that we actually have the actuals. So if somebody, how they performed, how long they performed on a position for a lot of our consultants, that's information that we've had in our systems for 20, 30 years. So having the actual data to compare against what people are saying now makes a big difference. It's something our competition can't go out and buy. >> Yeah, it's interesting. It's just so interesting a world you're in. You're like in the cross hairs of a lot of moving waves. Look at the HR world is changing significantly from the world I live in in tech, for instance, has been a big thing and making sure people are being promoted. And the old way of doing HR is like, processes are kind of broken but the tools are available. So there's a whole dynamic going on in the future of work that's overlaying on top of your job. How are you dealing with that? >> It's very difficult. We use a lot of natural language processing and machine learning algorithms to really look at people and almost in some ways predict their level of thought leadership. So it's not enough any more to say, "I have those skills." It's can they do more than the skill we're hiring for and are they really going to be able to come in here and be that curious person, that problem solver, right? We can teach people tools. How do you teach somebody to be a problem solver? >> I can almost imagine Claire and some of these automated intelligences, I call it AIs. To me, it's automated intelligence. AIs don't really exist, I mean Google's probably about. Neural networks that teach neural networks, c'mon. >> Salema: Right. >> That's 1980s. But the augmentation is the key, and you think about what you're doing is you almost want the system to be working for the user. So instead of HR, you flip it around. So the HR should be notified that, "Hey, Salema needs a promotion right now. "She's peaked, she's been growing." Now new openings are coming up. Rather than trying to have the review, have the end user fill out their performances, having an ongoing performance track is probably pretty key. >> Yeah, it's something that we look at in our applicant tracking systems and how we keep track of the people that are out there working for our clients and the feedback that we get. Survey information is really important to us, both from our customers and from our consultants. So we use that to help them grow, and I mentioned earlier, one of the things that we tried really hard is coming to work for Allegis is about coming to work for a family where you're not just making a living, but you're making a life. >> Alright, final question, well, two final questions. But I'll get your thoughts on the show, that's a little bit of an easier question. The pointed question here, relative to what you're doing is, the world now with Cloud and data is about scale. And one of the things that's interesting about what you guys are doing at your work is it's pretty large scale. You mentioned 55 million people and beyond that. A lot of folks have to operate now at a higher level of scale. >> Yeah. >> What's your advice to other practitioners out there that have to start thinking differently in terms of order of magnitude scale. Just mindset, what advice would you share with folks on the scale question? >> I would say collect the data. Collect all the data you create as an organization. Collect everything, and then over time, connect it. Connect the dot. I often say collect it and we'll connect it. And I think that start small, right? I mean, when you don't want to boil the ocean, but collecting the data with the tools that we have today with the big data appliances, we use Cloudera, Informatica, by bringing all of that data into our enterprise data hub, then as those business problems exist and we can slowly start to help the organization by being those problem solvers. >> Awesome, great success story. Final question, word for you is, what's the show like? For the folks watching? What's the experience like, what's the vibe? >> Salema: At Informatica World? >> Informatica World here in San Francisco? >> It's been amazing. It's full of energy like the opening yesterday had my heart racing. It's really been a great event. It's a lot smaller than some of the ones that I think people are accustomed to coming to. And because of that, you get more of that personal touch. The classrooms aren't so big that you can't do a question and answers. >> John: It's very intimate. You get to meet the executives, they're very transparent. >> Yeah, absolutely. And really just see where it's going. And this isn't the kind of thing where you're seeing something that's going to be here years from now. You're seeing what's going to be released weeks from now. >> You're happy with Informatica? They've done a good job with the product? >> Absolutely. I love Informatica. I love our partnership with them. I mentioned for me, it's about, they have a seat at our table, and they help us solve problems and things where we didn't think they were possible, and they really help us identify what those things are and how we can resolve them. >> What do you think about their transformation? >> I love it. I absolutely love it. I love all of the buzz words around here, and I even love the new logo. I think it's great, it's full of energy. >> John: Salema Rice, thanks so much for spending the time here. >> Absolutely. >> Inside the Cube, sharing her experiences as an industry practitioner also large scale. Really using data as an asset, that is the theme here. And of course, we believe at the Cube. We're very data-driven as well, software-defined. And that's the future. Salema, thank you so much, it's the Cube. More live coverage here in San Francisco with the Cube after this short break. I'm John Furrier, stay with us.