#DataFemme Learns from Google Looker
Lately, I’ve been inspired to provide written transcripts of my #DataFemme episodes with the much appreciated help of my summer intern, Kashvi :) It is very important to me that all consumers of content feel valued by DiKayo Data and have access to #DataFemme content in their preferred methods.
This episode, featuring Leigha Jarrett of Google, is available as a written transcript below:
Hello, everyone, and welcome to #DataFemme where we engage you with stories of how innovators across the globe are using data to achieve new heights in their respective industries. I'm Danielle, founder of Dikayo Data and I'm here with Leah Jarrett, who is a project manager at Google. She and I are going to talk a lot about how Google and especially Google Looker is helping companies upload their whole business to a cloud based system. We will also be talking about how to present the material that can inspire and ultimately motivate companies to reach the point where they feel it's necessary to update to this new world. I know that like me, you will love this episode with all of Leah's insights, and so feel free to tweet out, post wherever, you know, tell whomever you know about #DataFemme and the content that we're creating. As always, I want all of our content to be community based because we all learn better together. So without further ado, we will get to our episode.
DiKayo Data: Well, hey, Leigha, I'm really happy to have you on the show. Do you want to take a few minutes to introduce yourself before we get into the thick of it?
Leigha Jarett: My name is Leigha Jarett. I am based out of New York. I live in Brooklyn. I'm a big New York girl, big fan of Brooklyn. So shout out to my Brooklyn people.
DiKayo Data: Were you born there? I grew up in Manhattan…
Leigha Jarett: Oh, very cool. I was born on Long Island, but I moved to New York City, probably like six years ago, and we just love Brooklyn. Like we're gonna be here forever.
DiKayo Data: How could you not? Yeah, I love Brooklyn too
Leigha Jarett: Definitely. So, I work at Google. I specifically work at Looker, which is a data analytics product inside of Google Cloud. And I'm a product manager on the developer platform, which means that I try really hard to make Looker easy to use and powerful for software developers and web developers.
DiKayo Data: Tell me about your history in academia with your PhD and your MS in computer science. I'm really curious about that one, because it seems like as a developer, you would be using your computer science background a lot.
Leigha Jarett: Definitely. Yeah. So I had kind of a weird way of getting into my career. You know, when I was choosing what college I wanted to go to back in high school, I really loved art, and I was big into drawing and painting and even some digital art, so I really wanted to go to an art school, but I wasn't 100% set on having a career as an artist so I decided to go and I started off as doing a double major with like fine arts, and I just picked biology because I was like, “oh, I liked bio class. So I guess I'll just do that”, and after the first semester, I was like, “yeah, this art thing is actually really hard. My art classes are so much harder than my science and math classes”, and I realized that my favorite classes were actually my math and physics classes, so I decided to pivot into the school of engineering and I majored in biomedical engineering, and that was super fun. I really loved that field. I loved all my classes. I spent all of my summers doing these internships focused in research, most of them as part of like the NSF research grants and things like that. So when I got time to graduate, since I had all this research experience, going into a PhD just kind of seemed like a natural fit. And all of my exposure in the biomedical engineering world had really been to research scientists who had their PhDs, so I kinda, you know, jumped right into a PhD program that was based out in New York City joint between a couple of different schools, and I was focusing on computational biology, so really, I was using machine learning to try to do some analysis on cancer data to try to predict different types of cancer based on mutations that we would see in patients genomes so it's so so so different from what I do now, but like, you know, a year into the PhD program, I just felt like, you know, the academia wasn't for me, you know, that I think that the environment of academia can sometimes be competitive; you you have to be secretive about some of the research you're doing, because you don't want other people to publish before you. A lot of it is out of your control, you know, when you graduate is based on how much you've been able to research and publish. And a lot of that depends on results and when you're talking about biology, that's like, cells producing signals, it's stuff that's totally out of your control, and I am definitely a control freak. So I was pretty unhappy and just feeling burned out. You know, like, my whole life was just my research that I was doing and didn't have enough time to see friends or family. I didn't have any money to do things like travel or go out in New York, so I decided I was going to leave academia and go into industry, which is when I switched to data science, this all comes back to my computer science masters degree because I had spent a year in grad school, so I had some computer science and machine learning classes under my belt and I was like, “well may as well do something with these and finish up a masters degree”. So I finished up my masters degree in computer science while I was working the past couple years.
DiKayo Data: The way you came into data science is so unique. It's almost like you're meant to fall into it. Somehow people would call you that silly word, a unicorn, because you are a computer scientist and a data scientist who's done machine learning.
Leigha Jarett: In the data science world when I was in my PhD program, I actually used R extensively because all the biostatistics packages were all built in R, but when I switched to industry, and I started working as a data scientist for PepsiCo, I mostly use Python, I use SQL a lot as well. So I was mostly doing my analysis or building machine learning models using Python code, but we would often need to pull data to feed into that, and all of our data was living in a data warehouse, so that's where SQL came into play. When I was in my computer science masters degree, I took a couple of classes that required coding, but actually many more of them since I kind of had a lot of those credits under my belt already, a lot of the classes I took were more high level. So I took some user experience classes, I took some networking classes, I took classes on just like cloud technology. I wasn't doing as much hands on keyboard programming, as I did, you know, in my day to day job. And I guess more recently, I have started to try to pick up more of the front end stack, partially because I think it's just a good skill to have, but mostly because the part of the Looker product that I work on, which is called the extension framework allows our developers to build these custom UIs inside of the looker application. So they're using mostly React and JavaScript, so I felt like I really needed to learn those technologies to effectively understand their needs. So I would say now, those are the types of languages I use the most in my my day to day life, although I'll still pull out Python if I need to automate something once in a while.
DiKayo Data: So I know that you spend a lot of your time teaching people at companies how to leverage Google Cloud to migrate their business into a more cloud based system, since teaching in this way is a large part of your role and very important to you, I'd love you to tell me a bit more about the whole process.
Leigha Jarett: Yeah, totally, so that used to be really hard for me, because at some point in my career at Looker, I was a sales engineer. So I was responsible for selling Looker and really selling people on Looker's value and a lot of the companies that I was working with, specifically, when my territory was like the Midwest and Indiana, a lot of these companies are on outdated technology, or they were like really big companies that had like on premise servers and on premise databases running and here we are coming in being like, yeah, Looker born and bred in the cloud. Like I said, it's a data analytics tool, but it's completely web based. So you go to your URL, where you can log in, and then access your dashboards and see your data. And that's super different from a traditional business intelligence tool, where it's like a desktop application, like I have to go and I have to install this software onto my physical computer. It's hard to kind of get people to see the value with it, but luckily, the entire industry is like moving in there. I wouldn't even say it's moving in that direction, I would say it has already moved totally to that direction. So now it's a much easier sell and just convincing people that it's easier to collaborate, it's easier to maintain, and really, like I explained this to my grandpa somewhat recently, you know, the cloud is way more secure in my view than like me managing my own stuff. Like, I trust the bank to handle my data better or my money rather than myself just storing money under my mattress, because they're protecting everyone's money, and they're dealing with all this money, this is all that they worry about. So when I talk to people about moving things to the cloud, I'm like, these are companies that have so so so much data that they care about protecting and so much infrastructure that they need to manage. So they're gonna do a way way better job at managing it than you individually will.
DiKayo Data: I agree, it's like, even if you know quite a bit about investing, say, it's still so much more comfortable to have somebody you pay to, like do it on a regular basis, because it's like, even if I know something, I'm not gonna be able to keep up with it as much as somebody who just lives and breathes it every day. Now, there are so many opportunities to veer away from that responsibility, which types of companies are still presenting, you know, a hard sell to you with this Looker technology?
Leigha Jarett: Well, I think the data world in general, you know, there, it's always difficult to have conversations about data for healthcare and for finance companies. And I think it makes a lot of sense, this is super sensitive data that you're working with that can have really big implications on people's lives if that information gets out. So they're very, very security conscious. I wouldn't say that Looker is in a unique situation, I think that every time any data vendor is having a conversation with someone from healthcare, or finance, it's going to be more complex.
DiKayo Data: I agree. I mean, I've heard this from a lot of smaller companies that I've worked with, and they're just on a different competitive scale than you are because, you know, Looker is Google.
Leigha Jarett: Yeah, I think that definitely helps with lending credibility, especially when we're talking about, you know, like the data. So I think, largely, people tend to trust Google. Maybe I shouldn't make that assumption. I'm sure there are a lot of people out there who don't trust any big tech companies. But I think having a name that people recognize and use in their everyday lives, like people use Google search all the time, it really does lend that credibility just to have that initial conversation with someone. Whereas in the past, you know, maybe if you're a small startup, it might be harder just to even get your foot in the door at some of those larger institutions dealing with sensitive data.
DiKayo Data: When you're at that large a company, there's so many products that somebody will likely have tried another one and be able to speak to its validity and it is easier. You know, like, it's definitely easier when you can integrate, you know, something with all of your other mails, with all of your other domains, things like that. I can understand how Google's widespread influence could be helpful because they, you know, there's so many products for a variety of different experience levels, are you dealing at all with the whole no code, low code revolution, when you're marketing to people or trying to explain, you know, what they can do with your service?
Leigha Jarett: I think that's big on everyone's mind right now is kind of like these low code, no codes, I mean, Looker really isn't in that business, but Google does have other products like app sheets that are and I think at this point, it'll be interesting to see how business intelligence tools like Looker evolve with this, you know, new part of the industry and see, like, I'm curious to see if people start moving things that were previously done in business intelligence tools to these low code, or no code solutions, or if those just become another place to generate or consume data. So maybe it would be something that connects with Looker, or with a business intelligence tool to pull data from it or to push data to it. So I feel like right now, it hasn't come up so much, but I'm sure it will start to come up more and more as these tools gain a lot of attraction.
DiKayo Data: You've definitely gone really deep into machine learning and coding, and that's what you studied ,and oftentimes, there is a pretty big transition that people have to undergo between being deep in the raw data, and presenting it. I'm just curious, because of my background, and presenting how you became the spokesperson, which you are, and you know, what aspects of your personality or experience lead you to that because, you know, it's pretty rare to be deep in the code and also be the one that's able to present it the best.
Leigha Jarett: That's a fun question. So I feel like it's so strange to me that this has become such a big part of my job. I mean, I really do love presenting, and especially when it involves explaining technical concepts, but it's funny because like, as a kid, I was super shy and quiet and I still have a small voice, like people tell me all the time, you know, like, “oh, like Leigha, we can't really hear you because your voice is so quiet”, so I kind of feel like maybe the reason why I enjoy public speaking or I'm good at it is because it's like, you know, okay, people are paying attention to me, I don't have to, like scream and yell to get anyone's attention, which is kind of funny to me, but I do. I always felt like I was a good communicator, and the technical stuff didn't come super natural to me. I always enjoyed math, but I was never, you know, like the best math student. So I feel like I had to work much harder at learning the technical stuff, learning the coding, learning the math and statistics behind machine learning, but the communications and presentation that was more just I enjoyed doing this, this is something I feel like I'm good at and I think it might come from having to work so hard on those technical things. You know, if you have to spend a long time understanding things, or being a really hands on user, I feel like it's much easier than to teach those concepts to other people, which is one of my favorite parts of my job now.
DiKayo Data: You know, that's a really powerful way that you expressed that because I know from my own experience as a student, that I prefer to learn from somebody who might have struggled the same way that I am, and learning something really technical, because I trust that now they know how to explain it to me. Whereas somebody who's just been naturally primed to do it, views that topic on a different level and as a teacher, I can tell you, I'm a way better statistics teacher than I am a piano teacher. I learned how to play piano when I was very young and I don't really remember anything about the learning process and I wouldn't be able to tell you how it happened, it just happened, where statistics, I struggled the first time I came across it, and then the next time I came across it, it was the love of my life. My last curveball question came when you said that you are a visual artist, and I'm just wondering, now being in the tech field, what you think about this whole NFT craze.
Leigha Jarett: It's so crazy. I mean, you're on Twitter too, so I'm sure being anywhere in tech Twitter, it's just all over all of my like pages every time. I just log onto Twitter, it's something about NFT and I feel like at first, I was super interested, and I can definitely see the value in having this ledger of ownership, you know, and this historical record of ownership for a piece of art, but I have done a lot of digital art on my iPad, and around Christmas, we're all off from work, so I was like, ”I am going to just like make an NFT for my artwork, because I want to say I did it and just see what this whole thing is about”, and I started to go through the process, and I was just looking on OpenSea, and like some of the other NFT marketplaces, and I just kind of felt like, this isn't art, like a lot like some of it definitely is. I don't want to put anyone down, but a lot of it was built with a meaning behind it. You know, it was like a doodle that someone copied and pasted five times and changed the color on it, and then it was designated as a series so I was kind of like I had high hopes, I was kind of disappointed. I feel like there are so many people who are thinking of it as a get rich quick scheme that it may like, have put too much noise to like actually get to real art from those marketplaces, but maybe it will evolve and change, or maybe I'm just being like too harsh of a critic, you know, like everyone has their own taste in art, and I was like, this is not the kind of art that I like.
DiKayo Data: Well, honestly, I'm really glad to hear somebody with authority like you say that, because it makes me feel less weird about questioning the new fad. I definitely want to learn as much as I can about NFTs within reason because it is interesting, it is learning, and if they're relevant in a long term way I would never want to fall behind, but there's so much to learn even in our data science industry, and there's not unlimited time, and speaking of time, I want to be mindful of your time, so I'm going to ask you if there's anything else that you'd want to share with me or my Data Femme audience before we wrap till the next time.
Leigha Jarett: Yeah, no, this is great. Yeah, anyone I'm available on like Twitter and LinkedIn, I'm always happy to chat about data. So definitely reach out!