 Live from San Francisco, it's theCUBE, covering Informatica World 2017, brought to you by Informatica. Hey, welcome back everyone. We're here live in San Francisco for Informatica World 2017. This is theCUBE, so look at Angles flagship program. We go out to the events and extract the signal of the noise. I'm John Furrier, the host of theCUBE. Our next guest is Slima Rice, who's the Chief Data Officer, Global Enterprise Data and Analytics for Allegious Group. Slima, welcome to theCUBE. So you won the Informatica Innovation Award Monday night. Congratulations. Thank you. You're also a Chief Data Officer, which we love to have conversations with because really, what does that mean? 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 Allegious 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 transform the industry almost 30 years ago. So we 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, Allegious 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. So Allegious 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 75, a little over 75% of our organization. So you guys match talent that's in your network with opportunities that they're 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 services organizations, and we have managed service programs and vendor management solutions, and other operating companies that service other types of industries. So you mentioned as we were getting started that you used data as a competitive strategy weapon, if you will ask that key asset. I kind of weaved in competitive strategy. If you're doing well, that's a competitive strategy if you're successful. That's the key conversation here at Informatica World this year, and in the industry worldwide, is that as they look at the assets differently, it's not just an accounting thing. You most CFOs know where plant and equipment are and the depreciation schedule, amortization schedule, all that stuff's kind of like financial 101, but 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, from metaphorically speaking? And then also how do you deploy it as an asset? How does it get payback 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 in getting a job, there's probably not, maybe two other times in your life that are more traumatic, right? So birth, death, and, or, you know. My home. By getting married, maybe, 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 religious family and not just taking a job. Or a piece of our resume that's on file, so to speak. Take us through an example of a use case that someone could relate to whether you guys are 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. It's 55 million. 55 million resumes. 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 roles. 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, deer, placeholder, my name, wait, didn't insert my name. As it's 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. But similarly with your, it's a high touch and again, it's an intimate. Very much so. Very intimate to the user. How are 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. So 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 talent 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. We're slave to process. You want the process to work for you. Absolutely. And the second one is, you know, what we talk about in the startup community and growing companies is that, you know, you always see people, oh, she and he is a good fit. You know, being a good fit for a job really is key because you could be in a job and have to be unhappy and no one wins. That's right. So getting the fit is critical. So you guys are using humans with machines 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. Now, as you look back, and you're obviously your new cosmetic 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, the data wave that's happening. You're obviously a chief data officer. So you got to squint through the BS or the fog or the smoke screens 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, you know, data at the center and with software and with algorithms and obviously the mix of humans. What's the big industry trend that you like and what don't you like? Yeah, I love what Informatica is doing. I love that they're combining the best of artificial intelligence and machine learning into every application they create. That's really critical to us. And I think to every company is, you know, 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 Clarenal, 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 is bad for you. Clean data, good data is really instrumental. How are you guys refreshing the data? I mean, we were talking about someone from Informatica was on, talking about the heartbeat of data is different. But also, that implies that the heart is a critical organ, so you need a surgeon for that heart surgery. Sometimes data hygiene, you need a data hygienist. So there's a spectrum of data interaction points. What's your thoughts on data quality? I mean, 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 like 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, somebody on LinkedIn might put just 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. He brings 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 mean, I live in the Bay Area. I mean, I'm in the East Bay and I got to go to Palo Alto. It's a nightmare, but it 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 then trying to turn in social network was to do a decent job with. But that's more data. Reputation is super important in the world you're in. How are you guys looking at that? Because I can see how you guys got the blend of machines and humans. That's nice. The business philosophy is 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 I think that 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. I mean, it's just so interesting world you're in. You're like in the crosshairs of a lot of moving waves. I mean, look at the HR world is changing significantly from the world I live in, women in tech, for instance, has been a big thing and making sure people are being promoted in the old way of doing HR. It's like processes are kind of broken for some of the tools that are available. So there's a whole dynamic going on on the future of work that's overlaying on top of your job. How are you dealing with that? It's very difficult. I mean, 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, right? So it's not enough anymore to just 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? I mean, we can teach people tools. How do you teach somebody to be a problem solver? I can almost imagine Claire and some of these are augmented intelligence or I call it AI. To me, it's augmented intelligence. AI doesn't really exist. I mean, Google's not about neural networks. I teach neural networks. Come on, that's what I'm talking about. But the augmentation is the key. And I think 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, Salima 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, whether 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 try really hard is coming to work for Allegias is about coming to work for a family where you're not just making a living, but you're making a life. All right, final question. Oh, two final questions. I'll get your thoughts on the show. It's a little bit easier question. The pointed question here for 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 just beyond that. A lot of folks have to operate now at a higher level of scale. 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, you know, I often say collected and we'll connect it. And I think that starts 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 cloud era Informatica by bringing all of that data into our enterprise data hub, then as those business problems exist, then we can slowly start to help the organization by being those problem solvers. Awesome, great success story. Final question, word for you, final word is what's the show like for the folks watching? What is the? What's the experience like? What's the vibe? At Informatica World? Informatica World here in San Francisco. It's been amazing. I mean, it's full of energy, like the opening yesterday had my heart racing. It's really been, it's 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. It's very intimate. You can meet the executives. They're very transparent. Absolutely. And really just seeing 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. So I think this is the best. You're happy with Informatica. You've done a good job with the product. Absolutely. I love Informatica. I love our partnership with them. I mentioned like 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, you know, 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 buzzwords around here. And I even love the new logo. I think it's great. It's full of energy. Selima Rice, thanks so much for spending the time here on the theCUBE. 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 in theCUBE. We're very data driven as well. Software defined as, and that's the future. Selima, thanks so much. theCUBE, more live coverage here in San Francisco with theCUBE after this short break. I'm John Furrier. Stay with us.