>>Hey everyone. Welcome back to the cube. Lisa Martin, with Dave ante. We're here in Las Vegas with snowflake at the snowflake summit 22. This is the fourth annual there's close to 10,000 people here. Lots going on. Customers, partners, analysts, cross media, everyone talking about all of this news. We've got a couple of guests joining us. We're gonna unpack snow park. Torston grabs the director of product management at snowflake and Joe. No NTY AI and MDM architect at Allegis group. Guys. Welcome to the program. Thank >>You so much for having >>Us. Isn't it great to be back in person? It is. >>Oh, wonderful. Yes, it >>Is. Indeed. Joe, talk to us a little bit about Allegis group. What do you do? And then tell us a little bit about your role specifically. >>Well, Allegis group is a collection of OPCA operating companies that do staffing. We're one of the biggest staffing companies in north America. We have a presence in AMEA and in the APAC region. So we work to find people jobs, and we help get 'em staffed and we help companies find people and we help individuals find >>People incredibly important these days, excuse me, incredibly important. These days. It is >>Very, it very is right >>There. Tell me a little bit about your role. You are the AI and MDM architect. You wear a lot of hats. >>Okay. So I'm a architect and I support both of those verticals within the company. So I work, I have a set of engineers and data scientists that work with me on the AI side, and we build data science models and solutions that help support what the company wants to do, right? So we build it to make business business processes faster and more streamlined. And we really see snow park and Python helping us to accelerate that and accelerate that delivery. So we're very excited about it. >>Explain snow park for, for people. I mean, I look at it as this, this wonderful sandbox. You can bring your own developer tools in, but, but explain in your words what it >>Is. Yeah. So we got interested in, in snow park because increasingly the feedback was that everybody wants to interact with snowflake through SQL. There are other languages that they would prefer to use, including Java Scala and of course, Python. Right? So then this led down to the, our, our work into snow park where we're building an infrastructure that allows us to host other languages natively on the snowflake compute platform. And now here, what we're, what we just announced is snow park for Python in public preview. So now you have the ability to natively run Python code on snowflake and benefit from the thousands of packages and libraries that the open source community around Python has contributed over the years. And that's a huge benefit for data scientists. It is ML practitioners and data engineers, because those are the, the languages and packages that are popular with them. So yeah, we very much look forward to working with the likes of you and other data scientists and, and data engineers around the Python ecosystem. >>Yeah. And, and snow park helps reduce the architectural footprint and it makes the data pipelines a little easier and less complex. We have a, we had a pipeline and it works on DMV data. And we converted that entire pipeline from Python, running on a VM to directly running down on snowflake. Right. We were able to eliminate code because you don't have to worry about multi threading, right? Because we can just set the warehouse size through a task, no more multi threading, throw that code away. Don't need to do it anymore. Right. We get the same results, but the architecture to run that pipeline gets immensely easier because it's a store procedure that's already there. And implementing that calling to that store procedure is very easy. The architecture that we use today uses six different components just to be able to run that Python code on a VM within our ecosystem to make sure that it runs on time and is scheduled and all of that. Right. But with snowflake, with snowflake and snow park and snowflake Python, it's two components. It's the store procedure and our ETL tool calling it. >>Okay. So you've simplified that, that stack. Yes. And, and eliminated all the other stuff that you had to do that now Snowflake's doing, am I correct? That you're actually taking the application development stack and the analytics stack and bringing them together? Are they merging? >>I don't know. I think in a way I'm not real sure how I would answer that question to be quite honest. I think with stream lit, there's a little bit of application that's gonna be down there. So you could maybe start to say that I'd have to see how that carries out and what we do and what we produce to really give you an answer to that. But yeah, maybe in a >>Little bit. Well, the reason I asked you is because you talk, we always talk about injecting data into apps, injecting machine intelligence and ML and AI into apps, but there are two separate stacks today. Aren't they >>Certainly the two are getting closer >>To Python Python. It gets a little better. Explain that, >>Explain, explain how >>That I just like in the keynote, right? The other day was SRE. When she showed her sample application, you can start to see that cuz you can do some data pipelining and data building and then throw that into a training module within Python, right down inside a snowflake and have it sitting there. Then you can use something like stream lit to, to expose it to your users. Right? We were talking about that the other day, about how do you get an ML and AI, after you have it running in front of people, we have a model right now that is a Mo a predictive and prescriptive model of one of our top KPIs. Right. And right now we can show it to everybody in the company, but it's through a Jupyter notebook. How do I deliver it? How do I get it in the front of people? So they can use it well with what we saw was streamlet, right? It's a perfect match. And then we can compile it. It's right down there on snowflake. And it's completely easier time to delivery to production because since it's already part of snowflake, there's no architectural review, right. As long as the code passes code review, and it's not poorly written code and isn't using a library that's dangerous, right. It's a simple deployment to production. So because it's encapsulated inside of that snowflake environment, we have approval to just use it. However we see fit. >>It's very, so that code delivery, that code review has to occur irrespective of, you know, not always whatever you're running it on. Okay. So I get that. And, and, but you, it's a frictionless environment you're saying, right. What would you have had to do prior to snowflake that you don't have to do now? >>Well, one, it's a longer review process to allow me to push the solution into production, right. Because I have to explain to my InfoSec people, right? My other it's not >>Trusted. >>Well, well don't use that word. No. Right? It got, there are checks and balances in everything that we do, >>It has to be verified. And >>That's all, it's, it's part of the, the, what I like to call the good bureaucracy, right? Those processes are in place to help all of us stay protected. >>It's the checklist. Yeah. That you >>Gotta go to. >>That's all it is. It's like fly on a plane. You, >>But that checklist gets smaller. And sometimes it's just one box now with, with Python through snow park, running down on the snowflake platform. And that's, that's the real advantage because we can do things faster. Right? We can do things easier, right? We're doing some mathematical data science right now and we're doing it through SQL, but Python will open that up much easier and allow us to deliver faster and more accurate results and easier not to mention, we're gonna try to bolt on the hybrid tables to that afterwards. >>Oh, we had talk about that. So can you, and I don't, I don't need an exact metric, but when you say faster talking 10% faster, 20% faster, 50% path >>Faster, it really depends on the solution. >>Well, gimme a range of, of the worst case, best case. >>I, I really don't have that. I don't, I wish I did. I wish I had that for you, but I really don't have >>It. I mean, obviously it's meaningful. I mean, if >>It is meaningful, it >>Has a business impact. It'll >>Be FA I think what it will do is it will speed up our work inside of our iterations. So we can then, you know, look at the code sooner. Right. And evaluate it sooner, measure it sooner, measure it faster. >>So is it fair to say that as a result, you can do more. Yeah. That's to, >>We be able do more well, and it will enable more of our people because they're used to working in Python. >>Can you talk a little bit about, from an enablement perspective, let's go up the stack to the folks at Allegis who are on the front lines, helping people get jobs. What are some of the benefits that having snow park for Python under the hood, how does it facilitate them being able to get access to data, to deliver what they need to, to their clients? >>Well, I think what we would use snowflake for a Python for there is when we're building them tools to let them know whether or not a user or a piece of talent is already within our system. Right. Things like that. Right. That's how we would leverage that. But again, it's also new. We're still figuring out what solutions we would move to Python. We are, we have some targeted, like we're, I have developers that are waiting for this and they're, and they're in private preview. Now they're playing around with it. They're ready to start using it. They're ready to start doing some analytical work on it, to get some of our analytical work out of, out of GCP. Right. Because that's where it is right now. Right. But all the data's in snowflake and it just, but we need to move that down now and take the data outta the data wasn't in snowflake before. So there, so the dashboards are up in GCP, but now that we've moved all of that data down in, down in the snowflake, the team that did that, those analytical dashboards, they want to use Python because that's the way it's written right now. So it's an easier transformation, an easier migration off of GCP and get us into snow, doing everything in snowflake, which is what we want. >>So you're saying you're doing the visualization in GCP. Is that righting? >>It's just some dashboarding. That's all, >>Not even visualization. You won't even give for. You won't even give me that. Okay. Okay. But >>Cause it's not visualization. It's just some D boardings of numbers and percentages and things like that. It's no graphic >>And it doesn't make sense to run that in snowflake, in GCP, you could just move it into AWS or, or >>No, we, what we'll be able to do now is all that data before was in GCP and all that Python code was running in GCP. We've moved all that data outta GCP, and now it's in snowflake and now we're gonna work on taking those Python scripts that we thought we were gonna have to rewrite differently. Right. Because Python, wasn't available now that Python's available, we have an easier way of getting those dashboards back out to our people. >>Okay. But you're taking it outta GCP, putting it to snowflake where anywhere, >>Well, the, so we'll build the, we'll build those, those, those dashboards. And they'll actually be, they'll be displayed through Tableau, which is our enterprise >>Tool for that. Yeah. Sure. Okay. And then when you operationalize it it'll go. >>But the idea is it's an easier pathway for us to migrate our code, our existing code it's in Python, down into snowflake, have it run against snowflake. Right. And because all the data's there >>Because it's not a, not a going out and coming back in, it's all integrated. >>We want, we, we want our people working on the data in snowflake. We want, that's our data platform. That's where we want our analytics done. Right. We don't want, we don't want, 'em done in other places. We when get all that data down and we've, we've over our data cloud journey, we've worked really hard to move all of that data. We use out of existing systems on prem, and now we're attacking our, the data that's in GCP and making sure it's down. And it's not a lot of data. And we, we fixed it with one data. Pipeline exposes all that data down on, down in snowflake now. And we're just migrating our code down to work against the snowflake platform, which is what we want. >>Why are you excited about hybrid tables? What's what, what, what's the >>Potential hybrid tables I'm excited about? Because we, so some of the data science that we do inside of snowflake produces a set of results and there recommendations, well, we have to get those recommendations back to our people back into our, our talent management system. And there's just some delays. There's about an hour delay of delivering that data back to that team. Well, with hybrid tables, I can just write it to the hybrid table. And that hybrid table can be directly accessed from our talent management system, be for the recruiters and for the hiring managers, to be able to see those recommendations and near real time. And that that's the value. >>Yep. We learned that access to real time. Data it in recent years is no longer a nice to have. It's like a huge competitive differentiator for every industry, including yours guys. Thank you for joining David me on the program, talking about snow park for Python. What that announcement means, how Allegis is leveraging the technology. We look forward to hearing what comes when it's GA >>Yeah. We're looking forward to, to it. Nice >>Guys. Great. All right guys. Thank you for our guests and Dave ante. I'm Lisa Martin. You're watching the cubes coverage of snowflake summit 22 stick around. We'll be right back with our next guest.

>> 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.