(upbeat music) >> From New York, it's the Cube covering escape nineteen. (upbeat music) >> Hello, welcome back to the cube coverage here in New York city for the first inaugural multi-cloud conference called escape 2019, I'm John Furrier host of the Cube, we're here with Natalie Gholmieh who's the data manager of data and integration services at the university of California, San Diego campus, office, >> sprawling data center, >> Yes (laughing) >> tons of IT, a lot of challenges. Welcome. >> Yeah, thank you for having me. >> So, thanks for taking the time out, you're a practitioner, you're here. Why are you at this conference, what are you hoping to gain from here? What interests you here at the multi-cloud escape conference. >> This conference is very much within the spirit of what we're trying to do. Uh- we- our CIO has directives which is, to avoid locking and to do multi-vendor orchestration um, an, uh, to go with containers first and open source wherever possible. So- and, I, this conference pretty much speaks to all of that. >> So, this is a really interesting data point because it seems the common thread is data and cloud is an integration thing so people are trying to find that integration point so they can have multiple vendors, multiple clouds. It seems like the multi-vendor were all back in the old days where you had multiple vendors, had a regimeous environment, data seems to be the lynch pin in all this. That's what you do- >> Right. >> How do you think about this because it used to be the big database ran the world, now you have lots of databases. You have applications, >> Right. >> they're everywhere now. >> Yeah, data is born in multiple systems but the data is also an asset right now, to all of the organizations including the university so, um, what we want to try to accomplish is to, uh, get all of this data possibly in one place or in multiple places and to be able to, to do analytics on top of this and this is what the value added processing over the data. >> What's exciting to you these days in the university? You guys try to change the business, what, what- it could be technology. What are the cool things that you like that you're working with right now or that you envision emerging? >> Yeah, so my team is currently building a platform to do an integrat- um, all of the data integration and we are planning to offer, this platform as a service to Developers to streamline and standardize, application development as well as integration development within the central IT of university. So this is pretty much the most exciting thing that we're doing is putting together this platform that is quite complex It is a journey that we're taking Together with the people who are already operating existing systems, and so we are putting this new thing that we're operating in parallel and we will be migrating to that new platform over time. >> I'm sure containers are involved >> Troupernetties >> Yes >> To be part of it >> Mhm, Mhm, yeah so the platform has two parts There is the application, publishing with Gooddoctrine troupernetties And we have also the streaming side of it Where to build the data pipeline with open source tools like ApacheNinefive and Apache kafka. So um yes So this is going to be wiring the data pipelines from source to target and moving the data in real time In order to- >> And you see that as a nice way to keep uh an option to move from cloud to cloud >> Potentially since the platforms role is to decouple the infrastructure from devolepment that way you could spin a portion of the platform on any cloud pretty much and run your workload. Anywhere you want. >> So classic Dev ops, Separate infrustracture as code provide a codified layer >> Yeah, Yeah >> So let me ask you a question, How did you get into all this data business? I mean what attracted you to the data field? What's your story? Tell us your story. >> Ah, so the data, you know, I personally started I mean I was I had more of a networking background and then I became a sys Admin and then I got into the business of logging and log aggregation for machine data And then I was you know using that Data to create Dashboards of system health and you know data correlation and this is what exposed me, personally, first to the data world. And then I saw the value in, in doing all of this With data and the value is even more impactful to the business, when you're working with actual business data. And then Right so I'm very excited about that. >> So you were swimming in the first data lake before data lakes were data lakes. >> Yeah, right, for machine data >> And once you're in there you see value Data exhaust comes in as we used to say back in the day. During the Hedupe days. Data exhaust. So now that you're doing the business value is the conversations the same, or are they different conversations? Or is it still the same kind of data conversation? What's, or is the, job the same? Because you still have machine data applications are throwing off data. >> Right >> You have infrastructure data being thrown off You have new abstrac-New software layers >> Yes >> Is it the same or is it different? Describe the current situation. >> Eh, you know, maybe the concepts are the same Uh, but I think the, the logging machine data has more value to IT to give incites on how to improve your, uh SLA's and your you know within the scope of IT. But the business data really will impact the business, the whole entire University for us. So, One of the things we're doing on the business side with the business data is to provide some analytics on students um, the student data in order to um, increase their chances for success so getting all of that data. Doing some reports and pattern analytics. And then yeah, and then coaching students. >> Not a bad place to live in San Diego. >> Oh It's excellent >> Isn't it, the weather's always perfect >> Oh yeah. >> The marine layers not as bad L.A. you know, or is it? >> Yeah we do have a good. University- >> The marine layer. >> University is right on the coast. So yeah, sometimes its gloomy the whole entire day. = [John] Yeah, I love it there I wish I could have gone to school at the university of San Diego >> It is great, It's a great place to be >> Love to go see my friends at Lajolla Del mar. >> Yeah >> Beautiful areas, >> Yup >> Great country. Well thank you for coming on, and sharing your insights into multi-cloudism and that thinking. It seems you're very foundational right now. In this whole thinking there's no master plan yet. People are really having good conversations around how to set it all up. >> Yup >> The architecture, >> Right >> The role, >> Yup >> You see the same thing? >> Yes architecture is actually a very essential piece of it because you need to plan before you go if you go without planning I think your bill is going be Up the roof, so it yeah >> So you'll sink in the quicksand of the data lake And get sucked into the data swamp >> Yeah, Right, yeah so, architecture is a big piece of it Design, and then build and then continuous improvement That's a huge thing at UC of San Diego >> You know what I get excited about, Is I get excited about real time, how real time, time series data is becoming a big part of the application development and understanding the context between good data and bad data, is always a hard problem a hard tech problem6. >> Yeah that is true, yeah their are a lot of processes that, uh should be set around the data to make sure that data is clean, and it's, a good data set and all that >> If data's an asset then does it have a value? Does it have a balance sheet? Should we value the data? Is some data more valuable then others? That's a good question, huh >> That is a good question, but I don't know the answer to that. >> No one knows it's like we always ask the question I think that's going to, I think that's a future state where at some point data can be recognized but right now it's hard to tell what's valuable or not. >> I think the value is in the return services And the value added services, that you As an organization, can bring to your customer base. This is where the value is and if you want to put a dollar amount on that, eheh, I don't know It's not my job >> And of course multiden here, Multi-clouds All have it and of course thank you so much for coming on Special time conversation. Keep conversation here theCube coverage. Of the first inaugural multi-cloud conference call to escape nineteen. Where the industry best are coming together practitioners, entrepenures, founders, executives and thought leaders, talking about what multi-cloud really can be and foundationally what it needs to be in place and this is what happens here at these conferences Tons of hallway conversations Natalie thank you for spending the time. Cube coverage. I'm John Furrier, thanks for watching. (simple upbeat music)

[Announcer] - From New York, it's theCUBE! Covering ESCAPE/19. >> Hello, welcome back to theCUBE coverage here in New York City for the first inaugural Multi-Cloud Conference called ESCAPE/2019. I'm John Furrier, host of theCUBE. We're here with Natalie Gholmieh who is the Manager of Data and Integration Services at the University of California San Diego campus/office- sprawling data center, tons of IT, a lot of challenges, welcome. >> Yeah, thank you for having me. >> So, thanks for taking the time out. You're a practitioner, you're here. Why are you at this conference? What are you hoping to gain from here? What interests you here at the Multi-Cloud Escape Conference? >> So, this conference is very much within the spirit of what we're trying to do. Our CIO has directives which is to avoid lock-in, to do multi-vendor orchestration, to go with containers first, and open-source wherever possible. So, this conference pretty much speaks to all of that. >> So, this is a really interesting data point, because it seems the common thread is data. >> Mhmm. >> The cloud is an integration of things, so people are trying to find that integration point so they can have multiple vendors, multiple clouds. It seems like the multi-vendor world back in the old days, where you had multiple vendors, heterogeneous environment, data seems to be the linchpin in all this. >> Right, yes. >> That's what you do. >> Right. >> How do you think about this? Because it used to be that the big database ran the world, now you have lots of databases, you have applications. >> Right, yeah. >> Databases are everywhere now. >> Data is born in multiple systems, but the data is also an asset right now to all of the organizations, including the university. So, what we want to try to accomplish is to get all of this data possibly in one place, or in multiple places, and be able to do analytics on top of this, and this is what the value-added processing over the data. >> What's exciting to you these days in the University? You guys try to change the business, could be technology? What are the cool things that you like, that you're working with right now, or that you envision emerging? >> Yeah. So, my team is currently building a platform to do all of the data integration and we are planning to offer this platform as a service to developers to streamline and standardize application development, as well as integration development, within the central IT at the University. So this is pretty much the most exciting thing that we're doing, is putting together this platform that is quite complex, it is a journey that we're taking together with the people who already operate the existing systems. So we're putting up this new thing that we're operating in parallel and then we will be migrating to that new platform. >> I'm sure containers are involved, >> Yes. >> Kubernetes is a key part of it. >> Yes, mhmm. So, the platform has two parts. There is the application publishing with Docker and Kubernetes, and we also have the streaming side of it, to build the data pipeline with open-source tools like Apache NiFi and Apache Kafka. So this is going to be wiring the data pipelines from source to target and moving the data in real time in order to- >> And you see that as a nice way to keep an option to move from cloud to cloud? >> Potentially, since the platform's role is to decouple the infrastructure from development. That way, you could spin a portion of the platform on any cloud, pretty much, and run your workload anywhere you want. >> So classic DevOps. >> Yeah. >> Separate infrastructure as code, provide a codified layer. >> Yeah. >> So, let me ask you a question. How did you get into all this data business? I mean, what attracted you to the data field? What's your story? Tell us your story. >> So, the data, you know, I personally started, I mean, I had more of a networking background. Then I became Sys Admin, then I got into the business of logging and log aggregation for machine data, and then I was, you know, using that data to create dashboards of system health and, you know, data correlation, and this is what exposed me, personally, first to the data world, and then I saw the value in doing all of this with data, and the value is even more impactful to the business when you're working with actual business data. So I'm very excited about that. >> So you were swimming in the first data lake before data lakes were data lakes. >> Yes, yeah, right, for machine data. >> Once you're in there, you see value, the data exhaust comes in, as we used to say back in the day, data exhaust! >> Yeah. >> So, now that you're dealing with the business value, is the conversation the same? Or are they different conversations? Or is it still the same, kind of, data conversation? Or is the job the same? Because you still have machine data, applications are throwing off data, you have infrastructure data being thrown off, you have new software layers. >> Yes, yeah. >> Is it the same, or is it different? Describe your current situation. >> You know, maybe the concepts are the same, but I think the logging machine data has more value to IT to give insights on how to improve your SLAs, within the scope of IT, but the business data really will impact the business, the whole entire university for us. So, one of the things that we're doing on the business side with the business data is to provide some analytics on the student data in order to increase their chances for success. So, getting all of that data, doing some reports and pattern analytics, and then coaching the students. >> Not a bad place to live, in San Diego, is it? >> Oh, it's excellent. >> Weather's always perfect? >> Oh, yeah. >> Marine layer's not as bad as L.A., but, you know. >> Yeah. >> Or is it? >> No, we do have- The university is right on the coast, so yeah. Sometimes it's gloomy the whole entire day. >> I love it there. I wish I could've gone to school at the University of San Diego. >> It is great. It's a great place to be. >> Love to go down, see my friends in La Jolla, Del Mar, beautiful areas. Great country. >> Yeah. >> Well, thank you for coming on and sharing your insights into multi-cloud and some of the thinking. It seems to be very foundational right now in its whole thinking. >> Mhmm. >> There's no master plan yet. People are really having good conversations around how to set it all up. >> Yeah. >> The architecture. >> Right. >> The role. >> Yeah, yeah. >> Do you see the same thing? >> Yes, architecture is actually a very essential piece of it because you need to plan before you go. If you go without planning, I think your bill is going to be off the roof. >> Huge bill. >> Yeah. >> And you'll sink in the quicksand and the data lake and you can be sucked into the data swamp. >> Yeah. Right. Yeah. So, architecture is a big piece of it, design, then build, and then continuous improvement, that's a huge thing at UC San Diego. >> You know what I get excited about? I get excited about real time, and how real time, time series data is becoming a big part of the application development, and understanding the context between good data and bad data. >> Mhmm. >> It's always a hard problem. It's a hard tech problem. >> Yeah, that is true, yeah. There are a lot of processes that should be set around the data to make sure the data's clean and it's a good data set and all of that. >> If data's an asset, then has it got a value? Is it on the balance sheet? Shouldn't we value the data? Some data's more valuable than others? It's a good question, huh? >> It is a good question, but I don't know the answer to that. >> No one knows. We always ask the question. I think that's a future state where at some point, data can be recognized, but right now it's hard to tell what's valuable or not. >> I think the value is in the returned services and the value-added services that you, as an organization, can bring to your customer base. This is where the value is, and if you want to put a dollar amount on that, I don't know, it's not my job. >> Thank you so much for coming on, special time of conversation. >> Thank you. >> CUBE Conversation here, the CUBE Coverage of the first inaugural Multi-Cloud Conference called ESCAPE/19, where the industry best are coming together. Practitioners, entrepreneurs, founders, executives, and finally, just talking about what multi-cloud really can be, foundationally what needs to be in place. And this is what happens here at these conferences, tons of hallway conversations. Natalie, thank you for spending the time with us. CUBE Coverage, I'm John Furrier. Thanks for watching.

(light electronic music) >> Announcer: Live from Stanford University, in Paolo Alto, California, it's theCUBE. Covering Women in Data Science Conference, 2018. Brought to you by Stanford. >> Welcome back to theCUBE, I'm Lisa Martin. At Stanford University, we're here for the third annual Women in Data Science Conference. #WiDS2018, check it out, be part of the conversation, WiDS is in it's third year, but it's aiming to reach about a hundred thousand people this week alone. There's 177 regional WiDS events in 53 countries. This event here, the main event at Stanford, features key notes, technical vision talks, a career panel, and we're excited to be joined next by Dr. Nathalie Henry Riche. I did that in French. >> Yes. (laughs) Who is a researcher at Microsoft, and Natalie, first of all, welcome to theCUBE. >> Thank you, I'm really thrilled to be here. >> Yeah, you gave a technical vision talk on data visualization, and data driven's story telling. Share with our audience, some of the key messages, that the WiDS audience heard from you earlier today. >> Well, I guess, I gave two main messages. The first one is, that a visualization has two superpowers. >> Lisa: Superpowers? >> Superpowers. >> Tell me girl. The first one is enable you to kind of think about your data in a new way. So, just kind of form hypothesis, and answer questions you didn't even know, you had by your data. So, that's the first one. The second super power, is it's really useful to communicate information, and communicate with a large audience. Visualization helps you, kind of convey your point with data, to back it up. So, that's kind of the short one minute. >> I love that, super super hero, super power. So, WiDS is, as I mentioned at the intro, in its third year, and reaching, it's grown dramatically in such a short period of time. This is your first WiDS, and your first WiDS you are a speaker. What was is that attracted you to WiDS, and you went, yes I want to give some of my time to this, and come down from Seattle? >> Well, so I'm French originally, and my studies I did at engineering school, and it was one of three out of 300 men, right? >> Wow. >> So, I was requested a lot for women in computer science, and engineering. So, I actually really like it. Just meeting all of those people, talking about, you know, trying to bring more women in. Part of the job I'm doing is very creative, so, we're trying to come up with new ideas for visualization. I think having, you know, a wide range of people adds to the mix, and we get so many more exciting ideas. So, I really want to try to have more diverse group of people I can work with, and connect to, and so that's why that attracted me to here. >> Excellent, couple of things that you said I've heard a number of times today. The first one is, what Daniela went and shared, who's also a speaker, that often times, some of the few women in tech, and you mentioned being one of three in 300? Are asked to do a lot of other things. Did you find that, that, okay you're one of the few females, you're articulate, you like speaking, we want you to do all these things. >> Yes, and I say no a lot. (laughs) >> 'Cause I have kids, too. >> That's a skill, too. But yeah, it happens a lot. I think as we go further, it's going to be less and less happening. It's better in the end. So, it's kind of a service, I see it as a service to, you know, my field, and my company. But, at the same time, we'll also get a lot of benefits from it. But that said, I try to cut it down to a manageable level, so two hours flight from Seattle works great. >> Right, right, right. Another thing is that, that you mentioned the creativity. I've heard that a number of times, today from our guest Margot Gerritsen, was on as well. Tell me about your thoughts about being in this data science role, the need for creativity. How does, how it, why is that you might consider it, like a softer skill versus the technical skills. But, how important is that creativity in your job, for example? >> So, my job is really like researcher. Trying to have new ideas, and innovate for Microsoft in particular. So, I'm not really a data scientist, but I build the tools for a data scientist. So, knowing that, creativity is important because you need to kind of think out of the box. What is the next generation of tools that they will need? In turn, they need to think out of the box, kind of get more insight out of the data they're collecting. So, creativity is just like, pervasive to this whole data science thing. Problem solving as well, so you need a lot the left brain, and a lot of the right brain. Kind of both of them together. I think that having different cultures, and different genders, even different age ranges just, you know, makes you think out of the box. That's just what's happening. Discussing with people, I was discussing with someone in cosmology, and I was like, whoa. That brought up a lot of different ideas in me, so, to me, that's really critical part of what I'm doing every day. >> I like that, that kind of aligns to what one of our guests said earlier, and that is the thought diversity. Wow, I've never >> Yes. thought of thought diversity. But, you bring up a good point about it's not just about having women in the field, it's also having diversity, in terms of generations. One of the things that's, I think, pretty unique about WiDS, is it's not just about reaching young women in their first semester at University, for example. Maria Clavijo said that's the ideal time to really inspire. But, it's also reinvigorating women who've been in academia, or industry in stem subjects for a long time. So, you have, we have multiple generations, and to your point, that diversity is important, it's not just about gender, ethnicity. It's also about the diverse perspectives that come from being >> Exactly. from different generations. >> So, it's funny, 'cause I was giving this talk earlier, and it was, one part of it was about time line. When I was researching, you know how people draw time? Well there's, depending some culture, it goes from left to right, but some other culture it's front to back, back to front, right to left. So, we need to be aware of all of that, and it's so much easier to just have the people to converse with right in your office, or next door, to be aware of those. So, that's very important, especially to big companies, like Microsoft, 'cause of, you know, a lot of customers world wide. So, it's very important to just be immersed in that. >> Definitely. So, you have been published, you've got published research, and over 60 articles in leading venues, and human-computer interaction, and information visualization. But, something we chatted about off camera, was very intriguing about visualization and children. Tell me a little bit more about that. >> So, I happen to have two kids, you know, seven and four. I'm passionate about what I'm doing, and I just couldn't keep it out of their hands, right? So, I was just starting, you know, seeing what does my daughter learn at school, like, what does she learn in kindergarten? In fact, in kindergarten, I remember one day, she brought back candies, and I'm like did you get candies from school? She's like no, because we were doing a bar chart. I was like, what? (laughs) So, I was very intrigued in, you know, what do we teach, what do your kids learn? It was fascinating to see that, you know, from an early age, they learn how to do those visualizations. But, they don't really learn how you can lie with them, or you know, to kind of think critically about that. That, you know, maybe you can start your bar chart at two, and you know, you would have less candy, I guess. But, you could, kind of convey the wrong messages. So, I became passionate about this, and decided we need to just improve our teaching about how we can represent data, and how we can also misrepresent it. In the hope that for the next generation to come, they'll be able to look at a chart, and think critically about it. Whether or not it tells the right story with the right data. Kind of beyond, just picture's worth a thousand words, then I'm not going to think about it. >> Yeah. >> This is kind of my personal effort that I try to move myself forward. (chuckles) >> Well, it's so important about having that passion, and I think that's one of things that seems to be inherent about WiDS. Even, you know, yesterday seeing on the Twitter stream, WiDS New Zealand starting in five minutes, and it's been really focused on being so, kind of inclusive. Just sort of naturally, and one of the things that I learned in some of my prep for the show, is the bias that is still there, in data interpretation. You kind of talked about that, and I never really thought about it in that way. But, if a particular group of people is looking at a data set, and thinking it says this, and no other opinions, perspectives, thoughts are able to be incorporated to go, well, maybe it says this. >> Yeah. >> Then we're limiting ourselves in terms of one, the potential that the data has to, you know, help a business, create a new business model. But also, we're limiting our perspectives on making a massive social impact with data. >> Yeah, what I find very interesting is visualization often people think about it at the end of the spectrum. Like, I've collected my data, I analyze it, and now I need to pretty picture to kind of explain what I found. But, the most powerful use of visualization, I think, comes early on. Where you actually just collected your data, and you look at it before you run any statistical test. I did that not long ago with French air traffic data in the Hollands, I put them in, and I saw the little airplanes moving around. Then, what we saw, is one air planes doing loops like this. I was like, what is this going on, right? It was just a drone, doing like tests, right? But, somehow it got looped in into that data set. So, by looking at your data early on, you can detect what's wrong with the data. So then, when you actually run your statistical test, and your analysis, you better reflect what was that data in the first place, you know, what could go wrong there? So, I think inserting visualization early on is also critical to understand what we can really know, and do, and ask, about the data in the first place. >> So, it's kind of like, watching the story unfold, rather than going, we've done all this analysis here's the picture, the story is this. The story is, your sort of, turning it sort of page by page, it sounds like, and watching it, and interpreting it, as it's unfolding. >> Rethinking what you collected in the first place. Is that the right data you collected to answer the question you wanted to ask? Is it a good match or not? Then, rethink that, you know, collect new data, or the missing one, and then go on with your analysis. So, I think to me, it's really a thinking tool. >> It also sounds like another, we talked about the technical skills that had, obviously that a computer scientist, data scientist needs to have. But, there's other skills. Empathy, communication, collaboration. Sounds like also, there needs to be an ideal kind of skill set, it has to include open mindedness. >> Yes. >> Tell me a little bit about some of your experiences there, and not being married to, the data must say this. So, if it doesn't, I'm not going to look anywhere else. Where is open mindedness, in terms of being a critical skill set that needs to come to the field? >> Yeah, I mean we, that's that is totally a re-critical point. Think already, when you're collecting the data, especially as a scientist, when I run experiment, I kind of know what I want to find. Sometimes, you don't find it. You need to kind of embrace it. But, it's hard to have because sometimes, it's like those unconscious bias you have. Like, you're not really necessarily controlling them, and just the way you collected the data in the first place, maybe just, you know, skewed your result. So, it's very important to kind of think ahead of time of all of those bias you could have, and think about all of what could go wrong. Often, the scientific process is actually that trying to think about all of the stuff that could go wrong, and then check whether or not they're wrong. We're trying to infuse that, a little bit over Microsoft as well, kind of, you know, the data that we collect, can we analyze them, can we have teams of people who really think is that the right data? Are we collecting like, world-wide for example? Are we just collecting from the US? So, there's a lot of those, kind of, ethical, and bias, kind of training, and effort to try and remove that. The maximum from our work, and I think that it's across the entire world. I think, with all of this data collection everywhere, we kind of have to do that, very consciously. >> I think two things kind of speak to me that out of what you just said, that we've heard a number of times today. One, that failure, and I don't mean to say that failure is not a bad thing. That's how you, >> That's how you learn, Exactly, >> and grow. Exactly, in many ways it's not a bad F-word, it's this is how everybody that's successful got to wherever they are. But, it's also about embracing, as you said, the word embracing, embracing the fact that you might be bring bias into this, and you have to be okay with maybe this is the wrong data set. If you consider that a failure, consider it, to your point, a growth opportunity. That is one of the themes that we've heard today, and you've, kind of, elaborated on that. The second one is, be okay getting uncomfortable, get out of that comfort zone. Consciously uncomfortable, because when you're able to do that, the possibilities are limitless. >> Yes, and that's what I try to do everyday, 'cause I try to push all of the software that we're doing, and Microsoft is so big, you know, and all of those software are like so there. (laughs) So trying to come up with new ideas, like so many are failures, you know. Oh they won't make money, or they don't actually work when you, you know, for this population. So, most of my work is failure. (laughs) But hey, one success when you know why, and I'm happy about it. >> Exactly, but it's just charting that course to getting to the ah, this is the pot of gold at the end of the rainbow. Well Nathalie, thank you so much for taking some time to talk with us on theCUBE, and sharing your stories. Congratulations on being a speaker, your first WiDS, and we look forward to seeing you back next year. >> Thank you very much. >> We want to thank you for watching theCUBE. I'm Lisa Martin, live from WiDS 2018 at Stanford University. Stick around, I'll be back with my next guest after a short break. (light electronic music)