>>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.

>> Announcer: Live from San Francisco, it's The Cube covering Informatica World 2017. Brought to you by Informatica. >> Okay, welcome back everyone. We are here live in San Francisco for The Cube's exclusive coverage of Informatica World 2017. I'm John Furrier, this is Siliconangle's flagship program, we go out to the events and (he mumbles). My next guest is Jitesh Ghai who's the Vice President General Manager of data quality and governance for Informatica. Welcome to The Cube, thanks for joining us today. >> Happy to be here, John. Pleasure. >> So, two things right out of the gate. One, data quality and governance, two of the hottest topics in the industry, never mind within Informatica. You guys are announcing a lot of stuff, customers are pretty happy, you got a solid customer base. >> That's right. >> Product's been blooming, you got a big brand behind you now. This is important. There's laws now in place coming online in 2018, I think it's the GDPR. >> That's right. >> And there's a variety of other things, but more importantly customers got to get hold of their data. >> That's right. >> What's your take and what are you announcing here at the show? >> Well, you know, from a data governance and compliance and overall quality standpoint, data governance started off as a stick, a threat of regulatory pressure, but really the heart of what it is is effective access to and consumption of data, trusted data. And through that exercise of the threat of a stick, healthy practices have been implemented and that's resulted in an appreciation for data governance as a carrot, as an opportunity to innovate, innovate with your data to develop new business models. The challenge is as this maturation in the practice of data governance has happened there's been a realization that there's a lot of manual work, there's a lot of collaboration that's required across a cross-functional matrixed organization of stakeholders. And there's the concept of ... >> There's some dogma too, let's just face it, within organizations. I got all this data, I did it this way before. >> Right. >> And now, whoa, the pressure's on to make data work, right, I mean that's the big thing. >> That's exactly right. So, you collaborate, you align, and you agree on what data matters and how you govern it. But then you ultimately have to stop documenting your policies but actually make it real, implement it, and that's where the underlying data management stack comes into place. That could be making it real for regulatory, financial regulations, like BCBS 239 and CCAR, where data quality is essential. It could be making it real for security related regulations where protection is essential, like GDPR, the data protection regulation in the EU. And that's where, Informatica is launching a holistic enterprise data governance offering that enables you to not just document it, or as one CDO said to me, "You know, at some point you've got to stop talking about it, "you actually have to do it." To connecting the conceptual, the policies, with the underlying physical systems, which is where intelligent automation with the underlying data management portfolio, the industry-leading data management portfolio that we have, really delivers significant productivity benefits, it's really redefining the practice of data governance. >> Yeah, most people think of data as being one of those things, it's been kind of like, whether it's healthcare, HIPAA old models, it's always been an excuse to say no. "Whoa, we don't do it that way." Or, "Hey." It's kind of become a no-op kind of thing where, "No, we don't want to do any more than data." But you guys introduced CLAIR which is the acronym for the clairvoyant or AI, it's kind of a clever way to brand. >> That's right. >> That's going to bring in machine learning augmented intelligence and cool things. That only, to me, feels like you're speeding things up. >> That's exactly right. >> When in reality governance is more of a slowdown, so how do you blend the innovation strategy of making data freely available ... >> Right. >> ..and yet managing the control layer of governance, because governance wants to go slow, CLAIR wants to go fast, you know. Help me explain that. >> Well, in short, sometimes you have to go slow to go fast. And that's the heart of what our automated intelligence that CLAIR provides in the practice of data governance, is to ensure that people are getting access to, efficient access to trusted data and consuming it in the right context. And that's where you can set, you can define a set of policies, but ultimately you need those policies to connect to the right data assets within the enterprise. And to do that you need to be able to scan an entire enterprise's data sets to understand where all the data is and understand what that data is. >> Talk about the silver bullet that everyone just wants to buy, the answer to the test, which is ungettable, by the way, I believe, we just had Allegis on, one of your customers, and their differentiation to their competition is that they're using data as an asset but they're not going all algorithmic. There's the human data relationship. >> Absolutely. >> So there's really no silver bullet in data. You could use algorithms like machine learning to speed things up and work on things that are repeatal tasks. >> Right. >> Talk about that dynamic because governance can be accelerated with machine learning, I would imagine, right? >> Absolutely, absolutely. Governance is a practice of ensuring an understanding across people, processes and systems. And to do that you need to collaborate and define who are the people, what are your processes, and what are the systems that are most critical to you. Once you've defined that it's, well, how do we connect that to the underlying data assets that matter, and that's where machine learning really helps. Machine learning tells you that if you define customer id as a critical data element, through machine learning, through CLAIR, we are able to surface up everywhere in your organization where customer id resides. It could be cmd id, it could be customer_id, could be customer space id, cust id. Those are all the inferences we can make, the relationships we can make, and surface all of that up so that people have a clear understanding of where all these data assets reside. >> Jitesh, let's take a step back. I want to get your thoughts on this, I really want you to take a minute to explain something for the folks watching. So, there's a couple of different use cases, at least I've observed in a row and the wikibon team has certainly observed. Some people have an older definition of governance. >> Right. >> What's the current definition from your standpoint? What should people know about governance today that's different than just last year or even a few years ago, what's the new picture, what's the new narrative for governance and the impact to business? >> You know, it's a great question. I held a CDO summit in February, we had about 20 Chief Data Officers in New York and I just held an informal survey. "Who implements data governance programs "for regulatory reasons?" Everybody put their hand up. >> Yeah. >> And then I followed that up with, "Who implements data governance programs "to positively affect the top line?" and everybody put their hand up. That's the big transition that's happened in the industry is a realization that data governance is not just about compliance, it's also about effective policies to better understand your data, work with your data, and innovate with your data. Develop new business models, support your business in developing those new business models so that you can positively affect the top line. >> Another question we get up on The Cube all the time, and we also observe, and we've heard this here from other folks at Informatica and your customers have said, getting to know what you actually have is the first step. >> Right. >> Which sounds counter-intuitive but the reality is that a lot of folks realize there's an asset opportunity, they raise their, hey, top line revenue. I mean, who's not going to raise their hand on that one, right, you get fired. I mean, the reality is this train's coming down the tracks pretty fast, data as an input into value creation. >> That's exactly right. >> So now the first step is oh boy, just signed up for that, raise my hand, now what the hell do I have? >> Right. >> How do you react to that? What's your perspective on that? >> That's where you need to be able to, google indexed the internet to make it more consumable. Actually, a few search engines indexed the internet. Google came up with sophistication through its page-ranking algorithm. Similarly, we are cataloging the enterprise and through CLAIR we're making it so that the right relevant information is surfaced to the right practitioner. >> And that's the key. >> That is the key. >> Accelerating the access method, so increase the surface area of data, have the control catalog for the enterprise. >> That's right. >> Which is like your google search analogy. A little harder than searching the internet, but even google's not doing a great job these days, in my opinion, I should say that. But there's so many new data points coming in. >> That's right. >> So now the followup question is, okay, it's really hard when you start having IOT come in. >> That's right. >> Or gesture data or any kind of data coming in. How do you guys deal with that? How does that rock your world, as they say? >> And that's where effective consumption of data permeates across big data, cloud, as well as streaming data. We have implemented, in service to governance, we've implemented in-stream data quality rules to filter out the noise from the signal in sensor data coming in from aircraft subsystems, as an example. That's a means of, well, first you need to understand what are the events that matter, and that's a policy definition exercise which is a governance exercise. And then there's the implementation of filtering events in realtime so that you're only getting the signal and avoiding the noise, that's another IOT example. >> What's your big, take your Informatica hat off, put your kind of industry citizen hat on. >> Mm-hm. >> What's your view of the marketplace right now? What's the big wave that people are riding? Obviously, data, you could say data, don't say data 'cause we know that already. >> Sure. >> What should people, what do you observe out there in the marketplace that's different, that's changing very rapidly? Obviously we see Amazon stock going up like a hockey stick, obviously cloud is there. What are you getting excited about these days? >> You know, what I'm excited about is bringing broad-based access of data to the right users in the right context, and why that's exciting is because there's an appreciation that it's not the analytics that are important, it's the data that fuels those analytics that's important. 'Cause if you're not delivering trusted, accurate data it's effectively a garbage in, garbage out analytics problem. >> Hence the argument, data or algorithms, which one's more important? >> Right. >> I mean data is more important than algorithms 'cause algorithms need data. >> That's exactly right and that's even more true when you get into non-deterministic algorithms and when you get into machine learning. Your machine learning algorithm is only as good as the data you train it with. >> I mean look, machine learning is not a new thing. Unsupervised machine learning's getting better. >> Right. >> But that's really where the compute comes in, and the more data you have the more modeling you can do. These are new areas that are kind of coming online, so the question is, to you, what new exciting areas are energizing some of these old paradigms? We hear neural nets, I mean, google's just announced neural nets that teach neural nets to make machine learning easier for humans. >> Right. >> Okay. I mean, it has a little bit of computer science baseball but you're seeing machine learning now hitting mainstream. >> Right. >> What's the driver for all this? >> The driver for all this comes down to productivity and automation. It's productivity and automation in autonomous vehicles, it's productivity and automation that's now coming into smart homes, it's productivity and automation that is being introduced through data-driven transformation in the enterprise as well, right, that's the driver. >> It's so funny, one of my undergraduate computer science degrees was databases. And in the '80s it wasn't like you went out to the tub, "Hey, I'm a databaser." (He mimics uncertain mumbling) And now it's like the hottest thing, being a data guy. >> Right. >> And what's also interesting is a lot of the computer science programs have been energized by this whole software defined with cloud data because now they have unlimited, potentially, compute power. >> Right. >> What's your view on the young generation coming in as you look to hire and you look to interview people? What are some of the disciplines that are coming out of the universities and the masters programs that are different than it was even five years ago? What are some trends you're seeing in the young kids coming in, what are they gravitating towards? >> Well, you know, there's always an appreciation of, a greater appreciation for, you know, the phrase I love is, "In god we trust, all others must have data." There's an increasing growing culture around being data-driven. But from a background of young people, it's from a variety of backgrounds, of course computer science but philosophy majors, arts majors in general, all in service to the larger cause of making information more accessible, democratizing data, making it more consumable. >> I think AI, I agree, by the way, I would just add, I think AI, although it is hyped and I don't really want to burst that bubble because it's really promoting software. >> Right. >> I mean, AI's giving people a mental model of, "Oh my god, some pretty amazing things are happening." >> Sure. >> I mean, autonomous vehicles is what most people point to and say, "Hey, wow, that's pretty cool." A Tesla's much different than a classic car. I mean, you test-drive a TESLA you go, "Why am I buying BMW, Audi, Mercedes?" >> Right, exactly. >> It's a no brainer. >> Right. >> Except it's like (he mumbles), you got to get it installed. But, again, that's going to change pretty quickly. >> At this point it's becoming a table sticks exercise. If you're not innovating, if you're not applying intelligence and AI, you're not doing it right. >> Right, final question. What's your advice to your customers who are in the trenches, they raise their hand, they're committed to the mandate, they're going down the digital business transformation route, they recognize that data's the center of the value proposition, and they have to rethink and reimagine their businesses. >> Right. >> What advice do you give them in respect to how to think architecturally about data? >> Well, you know, it all starts with your data-driven transformations are only as good as the data that you're driving your transformations with. So, ensure that that's trusted data. Ensure that that's data you agree as an organization upon, not as a functional group, right. The definition of a customer in support is different from the definition of a customer in sales versus marketing. It's incredibly important to have a shared understanding, an alignment on what you are defining and what you're reporting against, because that's how you're running your business. >> So, the old schema concept, the old database world, know your types. >> Right. >> But then you got the unstructured data coming in as well, that's a tsunami IOT coming in. >> Sure, sure. >> That's going to be undefined, right? >> And the goal and the power of AI is to infer and extract metadata and meaning from this whole landscape of semi-structured and unstructured data. >> So you're of the opinion, I'm sure you're biased with being Informatica, but I'm just saying, I'm sure you're in favor of collect everything and connect the dots as you see fit. >> Well ... >> Or is that ...? >> It's a nuance, you can't collect everything but you can collect the metadata of everything. >> Metadata's important. >> Data that describes the data is what makes this achievable and doable, practically implementable. >> Jitesh Ghai here sharing the metadata, we're getting all the metadata from the industry, sharing it with you here on The Cube. I'm John Furrier here live at Informatica World 2017, exclusive Cube coverage, this is our third year. Go to siliconangle.com, check us out there, and also wikibon.com for our great research. Youtube.com/siliconangle for all the videos. More live coverage here at Informatica World in San Francisco after this short break, stay with us.

>> Narrator: 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 the Cube's exclusive coverage of Informatica World 2017. I'm John Furrier of SiliconAngle Media. My cohost, Peter Burris, head of research at SiliconAngle Media as well as the general manager of Wikibon.com, Wikibon research, check it out. Some great research there on IoT, big data, and certainly cloud computing. Our next guest is Graeme Thompson, Executive Vice President and Chief Information Officer for Informatica, great to see you, welcome back to the Cube. >> Nice to see you, John. >> Conference here, lot of customers, you've got an executive summit, dinner last night, you're kind of like the sounding board, they go to you for the checkpoint, hey, does this story jive, what's going on internally, 'cause you're living through a transformation as well at Informatica. Your customers are going through a transformation as well. We're at this tipping point. What's your take so far of the conference, and is that still the case? Anything you'd like to share on that would be great. >> Yeah, I mean we're proud to have some of the world's best companies using our products to do meaningful and important things. And the scale that some of these companies are doing it at is just staggering. I met with someone last night at dinner and, at Allegis, the talent management organization, and they process and keep up to date 55 million resumes every day. And they extract the metadata from those resumes to match the right candidate to the right job. And you know, that's interesting for them as a company but the societal impact of that is significant. Imagine, I mean we're all starved for talent, and you're matching the right talent with the right opportunity more often than not, using the intelligence of the data, it's pretty interesting. And then of course, I know you had Andrew McIntyre from the Cubs on yesterday, I mean how can you not love that story of how an organization as great and renown as the Cubs is using data to transform it's business operation. It's really amazing. >> We had Bruce Chizen on who's Executive Chairman of the Board of Informatica, was on the board at Oracle, but Peter asked him an interesting question that I'll ask you. What's your definition of strategic data management? >> That's a good one, so the way I define it is, if the basis of your competition is on digital assets compared to physical assets. So we're no longer dealing with plant or machinery or even capital, it's digital assets. If that is the basis of your competition, then the data that you rely on is the very foundation of that. And then it becomes strategic just like money is strategic. And the access to talent is strategic. The ability to leverage the data within your company, about your company, is strategic, and you have to be able to do it on-prem, you have to be able to do it on the cloud, you have to be able to do it in the real world where most of us live, which is in both worlds. And that to me, that's what makes it strategic. >> But let me build on that Graeme, 'cause in many respects the whole concept of digital transformation is, oh let me step back. One of the premises of business is to try to reduce what's known in financial or economic worlds as an asset specificity. So traditionally we've looked at assets and said, this asset's going to be applied to that use, and this asset's going to be applied to that use, and if it's the use isn't needed or it's not being applied, you lose the value of the asset. One of the basic premises of digital business and business generally is how to we reduce asset specificity, and data let's us do that by turning an aircraft engine into a service, we have transformed the role that that asset plays in our customer's business. So you're absolutely right, it's the ratio of physical to digital assets, but all businesses have to find ways to reduce their asset specificity by adding digital on top of it so they can appropriate that asset to a lot of new purposes. Do you agree with that? >> Absolutely, so take, so I know you talked to Sally about the data leak. So take a user case like customer support. Who in a software company knows more about the customer, what product their running, what version of product their running, what they're using it for because of the connectors they have. Nobody in the company knows more about that than the customer support organization. But that asset, the most profitable use of that information, may be in marketing, because then we can help our customers adopt something more quickly, we can help them get value from it more quickly. And it helps us because it helps us focus our R&D effort where the customers are really using the product instead of having to guess. So I think you're spot on, if you can remove the constraint on the asset to be for who paid for it, for one particular purpose and make it available to the entire enterprise and outside the enterprise, then you really start to see the value. >> The thing that you mentioned about digital assets Peter, and the Wikibon team talk about this all the time in their research, digital assets, is the data. Whether it's content or whatever. Certainly we're in the content business, but... >> Peter: Well digital assets are data. >> Are data, exactly, and whether it's content or whatever aspect it is. So I've got to ask you... >> Software, software is a digital asset. >> Data is at the center of it all. So I've got to ask you, there's been a lot of artificial intelligence watching going on in the industry. I call it augmented intelligence because it's really not yet artificial by the strictest, purest definition, but machine learning is very relevant. We talked about IoT when you were last in our studio. How is it impacting your business and customer's business? Because that's the real proof in the pudding, if you will. And customers are trying to sift through the BS that they're hearing from other folks. I'm not saying that you guys are saying BS, but what's the acid test? How do you differentiate between smokescreen and real deal? >> I think it comes down to, like any other technology investment, is what is the business outcome that it generated? So if you're trying to... So humans make mistakes, if you're trying to eliminate human error from a process, a machine can execute that process more repeatably and more accurately than a human. It's not about reducing cost, that's only semi-interesting. It's about enabling outcomes that weren't possible before. So you think about healthcare industry. Everyone talks about self-driving cars and how safer it'll be if the cars aren't dependent on a human, but one thing I read recently is we kill more people in the US by prescribing the wrong drug or the wrong dosage than we do on the roads. So humans work hard, but they make mistakes. If we can have the machine do that job because a human can tell it how to do the job and it can learn over time, then you can eliminate that error. And we're able to do things that we can only imagine. >> Machines rarely get tired, they rarely lose attention, blah blah blah blah blah, and it's all those things, and that's where the augmentation is. And there will be the other forms of artificial intelligence, the algorithms have been around for a long time. The hardware now can support it, and the data is being generated to apply it. >> The data's available and the cost of compute is approaching zero. So we're able to do things that the government could only do before. >> Graeme, I want to get your thoughts on data integration. Certainly we saw yesterday the news with Google Spanner. You guys were one of three companies that was early on, before they announced their general release of Spanner Worldwide, the attributed database, horizontally scaled database. Big deal, but you guys were also on the front end of that as it says in their blog post, and you guys are really strong at data integration. What are some of the challenges that the customers face with integration? What are the key things? Because that seems to be, whether you go multi-cloud or hybrid-cloud today, which is a gateway to multi-cloud, which is happening pretty fast, data integration is pretty important. >> Yes, so as a CIO this is something that is a very hot topic for me, and it's not a new hot topic, it was a hot topic 15 years ago when we went nuts and deployed all these client server applications because they were cheap and easy. And then you had to think about, oh these different disconnected applications don't serve an end-to-end process anymore, now we have to stitch them all together. That was hard, but it was all on-prem and you had access to it all. >> Peter: It was all programed. >> Right, whereas now, like you said you've got Salesforce, you've got Workday, you've got Great People, you've got your on-prem stuff, you've got applications that you're hosting on someone's PAS cloud and the IAS cloud and the SAS cloud, but to execute an end-to-end business process to generate an outcome you have to tie it all together. So instead of thinking about... >> John: And it's not on-prem so you can't touch it, and it's not on, you don't have it. >> Right so you can't hand code that, you could, but I would argue that that would be an unintelligent way to do it, which is where Microservices API has come in. So you can leverage the R&D efforts that the great software vendors like Salesforce create for us. And then you use Microservices to plug into that instead of having an army of people hand-coding interfaces, which is what we used to do 15 years ago. >> That's the human error point. I mean, it could be spaghetti code, all kinds of errors could happen. >> But also the maintenance of that is just virtually impossible given the speed and the fact that human beings are now thinking about new ways of doing things. You just can't keep up with that. >> I mean the coding thing's a big deal. We used to call it, back in the day, spaghetti code cause it's like all this integrated purpose-built coding for one purpose to glue it together. >> Right and then you change one data element and you have to rewrite or retest the whole thing. >> John: A guy leaves or a girl leaves, it's a nightmare, right? With APIs and Microservices you're decoupling that. That's kind of what I think you're getting at, right? >> Exactly, and that's what the whole iPass space is about. You can decouple the user experience from the data and just have, what does a user have to do, and then Microservices and APIs will take care of the work behind the scenes between the applications and that really lets... There's this concept of a citizen integrator. So 15 years ago, it was kind of a modern thought to have business people write reports. I think it won't be long before we'll be able to give the business teams the ability to do integration between applications without depending on me. >> I was talking with a young developer the other day and I'm like, yeah you know your coding is like me doing PowerPoints. They're like, what do you mean, it's so easy. No, it's not that easy. >> Well we've been building macros, good or bad, inside for example things like Excel for a long time and one of the primary drivers, in fact of a lot of the BI stuff, was citizen coders building macros and said I need the data to make my little macro run. Now I don't want to say that that is... That's not what we're talking about, we're talking about something that's considerably more robust where we can be very very creative in thinking about how we might use the data. And then being able to discover it and find it and very quickly and with a low-code orientation being able to make the actual application happen that has consequential impact in the marketplace. So Graeme, you're in a company that's trying to help customers move through some of these transitions. You're in a crucial role because we know where the data is, we know how to integrate it. >> Graeme: You did? >> Well we're discovering where the data is, we have tools that's going to help us, we're learning how to integrate it. But one of the big challenges is to get the business to adopt new orientations to the role that data's going to play. That to me is one of the key roles of the CIO, having worked with a lot of CIOs over the years. For a very very simple example, agile development does not line up with annual budget finance. How are you with Informatica helping to acculturate executive teams to think through new processes, new approaches to doing these things so that the business is better able to use the data so that consequential action happens as these concepts of these great insights that you're generating? >> So the whole change in management effort is a huge and complex thing to overcome. But I have a personal passion about making sure that you always remind people why they're doing it. Too often as product people or technologists, we get into the how and the what and we forget the why. And as soon as it gets difficult people abandon because it starts to get too hard, it starts to get painful, and if they've lost sight of the big why they're not going to role their sleeves up and gut it out and get through the process. So that's the first thing you have to do is remind them that the prize at the end is worth the pain. And it will be painful because no longer are you optimizing just your function. You have to think about what happens upstream from you, what happens downstream from you, and try and optimize things at the enterprise level. And that's not how most people were brought up. It's not how their measured, it's not how their compensated, but that's what's really required if you're going to make that transformation I think end-to-end. >> But it's also, even our language, we talk about innovation in this industry as though it was synonymous with just creating something new. Certainly our research very strongly shows that there's a difference between inventing something which is an engineering act and innovating around something which is a social act. Exactly what you just said. How do we get people to adopt things and change behaviors and fully utilize something and embed it within their practices so that we get derivative innovation and all of the other stuff that we're looking for? >> Yeah there's no easy recipe. People are different so people require a different story in order to have them buy in. Some people are loss-framed people, where you got to explain here's what's going to be bad if you don't do this. Other people are gain-framed people where you can say if we can accomplish this, we'll be able to do these great things. And it would be great if everyone was the same and one story worked for everyone, but it doesn't. So it's almost a feet on the street. Go talk to people and just keep reminding everyone why you're doing this and why it's going to be worth it. >> Peter: A little bit of behavioral economics there. >> John: Graeme I want to ask you one final question. You mention client server and how it was easy on-prem in the old days, get your arms around things, which is the IT practice, you know? That's the way it was done. In the cloud, a little bit more complex. But to take that a little step further, I want to get your thoughts on something. You lived through the world of server sprawl. More servers, more glue, you get your arms around it but then it got bloated, IT got bloated. And that's one of the catalysts for going to the cloud is efficiencies, bottom-line costs. But now, top line revenue now is a mandate. So now we have SAS sprawl. So with APIs, a little bit more security concern, but your thoughts on the now we have a SASification happening or API economy. So you have a lot more APIs, there's Microservices coming on the scene, it's emerging very quickly, still emergent. Embryonic some will say, not so, but I think it's embryonic still. Okay server sprawl, client server, VM sprawl, now you got SAS sprawl. Your thoughts on this dynamic and how a CIO tackles that? >> Yes, so it's the modern equivalent of your legacy technical debt. So it's a modern mess instead of an old mess, but it's the same problem. You know, you have to stitch these applications together and it's made worse by the ease of consuming these SAS applications. So one business function can go off and buy an application that's just for them, and the adjacent business function goes off and buys another application that's just for them. And before you know where you are, you're single sign-on page has three pages because you've got so many applications that you're using to run your business. So I think we have to be more thoughtful and not make the same mistake that we made after 2000 when we went nuts on all these client server applications and make sure that we're thinking about the end-to-end business outcome. >> John: So the unification layer is what, Identity, is it the data? I mean how do you think about that just conceptually? >> Well I think you still need a sensible portfolio of applications. I don't advocate that you just go buy every great application that's out there. If your business doesn't compete based on the capability that that application provides, you've got no business innovating. Just be as good as the next guy. But if you compete based on something, go pick the very best application you can but deploy it thoughtfully. Make sure it's integrated, make sure it serves the end-to-end... >> Well I'm also fascinated by the role that Clair might play here at going and looking at the metadata associated with some of these SAS applications to help us identify patterns and utilization. I think Clair and the thing that was announced here actually could have an impact in thinking about some of these things. >> The Clairvoyant app is a great one, Clair, I mean... She, he, it's vendor neutral, that's a whole different story, only kidding. Final thought Graeme on this show? Just color perspective, what's your thought so far just on the show vibe for the folks who aren't here, what's it like? >> So when you and I met a couple weeks ago we talked about the fact that I'd just joined the company just after last year's show. So I have nothing to compare it to, but the energy level is phenomenal. The feedback from the customer's I've talked to just reinforces that we have really really important customers and we're really important to them. You know, the customers are the ones driving this digital transformation and we're proud to be helping them. And every conversation I've had with customers has really reinforced that and it's great, I can't wait to get back to the office. >> And as we say the KPI, the metric of the transformation of the world is not quadrants or category winners, it's customer wins. >> Graeme: Absolutely. >> And I think that's a great point. Graeme Thompson, Executive Vice President and Chief Information Officer of Informatica sharing his insight. He is an integral part of their transformation as well as his customers. Informatica World coverage with the Cube continues. I'm John Furrier with Peter Burris with Wikimon.com. We'll be back with more, stay with us after this short break. (electronic music)