 Morning guys and girls, welcome back to theCUBE's live coverage of women in data science, WIDS 2023, live at Stanford University, Lisa Martin here with my co-host for this segment, Tracy Zhang. We're really excited to be talking with a great female rock star. You're going to learn a lot from her next, Jacqueline Quo, Solutions Engineer at DataIcoo. Welcome, Jacqueline, great to have you. Thank you so much. Thank you for being here. I'm so excited to be here. So one of the things I have to start out with is my mom, Kathy Daly, is watching. She's a New Yorker. You are a born and raised New Yorker and I learned from my mom and others that if you're born in New York, no matter how long you've moved away, you are a New Yorker. There's like, you guys have like a secret club. I am definitely very proud of being born and raised in New York. My family immigrated to New York, New Jersey from Taiwan. So very proud, Taiwanese American as well, but I absolutely love New York and I can't imagine living anywhere else. Yeah, I love it. So you studied, I was doing some research on it. You studied mechanical engineering at MIT. Yes. And you discovered your passion for all things data related. You worked at IBM as an analytics consultant. Talk to us a little bit about your career path. Were you always interested in engineering, STEM related subjects from the time you were a child? I feel like my interests were ranging in many different things and I ended up landing in engineering because I felt like I wanted to gain a toolkit, like a tool set to make some sort of change with, or use my career to make some sort of change in this world. And I landed on engineering and mechanical engineering specifically because I felt like I got to, in my undergrad, do a lot of hands-on projects, learn every part of the engineering and design process to build products, which is super transferable and transferable skills sort of is like the trend in my career so far, where after undergrad, I wanted to move back to New York and mechanical engineering jobs are kind of few and far in between in the city and I ended up landing at IBM doing analytics consulting because I wanted to understand how to use data. I knew that data was really powerful and I knew that working with it could allow me to tell better stories, to influence people across different industries and that's also how I kind of landed at Dataicoo to my current role because it really does allow me to work across different industries and work on different problems that are just interesting. Yeah, I like the way that, how you mentioned like building a toolkit when like doing your studies at school, do you think a lot of skills are still very relevant to your job at Dataicoo right now? I think that at the core of it is just problem solving and asking questions and continuing to be curious or trying to challenge what is currently given to you and I think in an engineering degree, you get a lot of that, but I think that you, we've actually seen that a lot in the panels today already that you get that through all different types of work and research and that kind of thoughtfulness comes across in all different industries too. Talk a little bit about some of the challenges you know that data science is solving because every company these days, whether it's an enterprise in manufacturing or a small business in retail, everybody has to be data driven because the end user, the end customer, whoever that is, whether it's a person, an individual, a company, a B2B, expects to have a personalized customer experience and that comes from data, but you have to be able to understand that data treat it properly, responsibly. Talk about some of the interesting projects that you're doing at Dataicoo or maybe some that you've done in the past that are really kind of transformative across things like climate change or police violence, some of the things that data science really is impacting these days. Yeah, absolutely. I think that what I love about coming to these conferences is that you hear about those really impactful social impact projects that I think everybody who's in data science wants to be working on and I think at Dataicoo, what's great is that we do have this program called IkiGuy where we work with nonprofits and we support them in their data and analytics projects and so a project I worked on was with the clean water. Oh my goodness, the Ocean Cleanup Project, Ocean Cleanup Organization, which was amazing because it was sort of outside of my day-to-day and it allowed me to work with them and help them understand better where plastic is being aggregated across the world and where it appears, like whether that's on beaches or in lakes and rivers, so using data to help them better understand that. I feel like from a day-to-day though, we, in terms of our customers, they're really looking at very basic problems with data and I say basic, not to diminish it, but really just to say that it's high impact but basic problems around how do they forecast sales better? That's a really basic problem but it's actually super complex and really impactful for people, for companies when it comes to forecasting how much headcount they need to have in the next year or how much inventory to have if they're retail and all of those are going to, especially for smaller companies, make a huge impact on whether they make profit or not. And so what's great about working at Dataac is you get to work on these high impact projects and oftentimes I think from my perspective I work as a solutions engineer on the commercial team so it's just we work generally with smaller customers and sometimes talking to them, me talking to them is like their first introduction to what data science is and what they can do with that data and sort of using our platform to show them what the possibilities are and help them build a strategy around how they can implement data in their data today. What's the difference? You were a data scientist by title and function, now you're a solutions engineer, talk about the ascendancy into that and also some of the things that you and Tracy have talked about is those transferable, those transportable skills that probably, maybe you learned in engineering, you brought data science now you're bringing to solutions engineering. Yeah, absolutely. So data science, I love working with data, I love getting in the weeds of things and I love like, oftentimes that means debugging things or looking line by line at your code and trying to make it better. I found that in the data science role while those things I really loved, sometimes it also meant that I couldn't see or didn't have visibility into the broader picture of well like, well why are we doing this project and like who is it impacting and because oftentimes your day to day is like very much in the weeds and so I moved into sales or solutions engineering at data IQ to get that perspective because what a sales engineering or sales engineer does is support the sale from a technical perspective and so you really truly understand well, what is the customer looking for and what is going to influence them to make a purchase and how do you tell the story of like the impact of data because oftentimes they need to quantify well, if I purchase a software like data IQ then I'm able to build this project and make this impact on the business and that is really powerful. That's where like the storytelling comes in and that's, I feel like a lot of what we've been hearing today about connecting data with people who can actually do something with that data. That's really the bridge that we as sales engineers are trying to connect in that sales process. So all about connectivity, isn't it? Yeah, definitely. We were talking about this earlier that like it's about making an impact and it's about like people who you're like analyzing data is like influencing and I saw that one of the keywords or like one of the biggest thing at data IQ is everyday AI so I wanted to just ask like could you please talk more about how does that weave into the problem solving and then like day to day making an impact process. Yes, so I started working on data who like around three years ago and I fell in love with the product itself. The product that we have is, we allow for people with different backgrounds if you're coming from a data analyst background, data science, data engineering, maybe you're more of like a business subject matter expert to all work in one unified central platform, one user interface and why that's powerful is that when you're working with data it's not just that data scientist working on their own and their own computer like coding, right? We've heard today that it's all about connecting the data scientists with those business people with maybe the data engineers and IT people who are actually going to put that model into production or other folks and so they all use different languages. Data scientists might use Python and R, your business people are using PowerPoint and Excel, everyone's using different tools. How do we bring them all in one place so that you can have conversations faster so that the business people can understand exactly what you're building with the data and can get their hands on that data and that model prediction faster. So that's like what DataIQ does, that's the product that we have and I completely forgot your question because I got so invested in talking about this. Oh, everyday AI. Yeah, so the goal of DataIQ is really to allow for those maybe less technical people with less traditional data science backgrounds, maybe they're like data experts and they understand the data really well and they've been working in SQL for all their career or maybe they're just subject matter experts and want to get more into working with data. We allow those people to do that through our no and low code tools within our platform. Platform is very visual as well and so I've seen a lot of people learn data science, learn machine learning by working in the tool itself and that's where everyday AI comes in because we truly believe that there are a lot of, there's a lot of unutilized expertise out there that we can bring in and if we did give them access to data, imagine what we could do and the kind of work that they can do and become empowered basically with that. Yeah, absolutely, we're just scratching the surface. I find data science so fascinating, especially when you talk about some of the real world applications, police violence, health inequities, climate change. Here we are in California and I don't know if you know we're experiencing an atmospheric river again tomorrow. Californians in rain, we are not good, I'm a native Californian, but we all know about climate change. People probably don't associate all of the data that is helping us understand it, make decisions based on what's coming, what's happened in the past. I just find that so fascinating but I really think we're truly at the beginning of really understanding the impact that being data driven can actually mean whether you are investigating climate change or police violence or health inequities or you're a grocery store that needs to become data driven because your consumer is expecting a personalized relevant experience, I want you to offer me up things that I know. I was doing online grocery shopping yesterday, just go back from Europe and I was so thankful that my grocery is data driven because they made the process so easy for me and but we have that expectation as consumers that it's going to be that easy, it's going to be that personalized and what a lot of folks don't understand is the data, the democratization of data, the AI that's helping make that a possibility that makes our lives easier. Yeah, and I love that point around like, data is everywhere and it's like the more we have, the actually the more access we actually are providing because now like compute is cheaper, like data is literally everywhere you can get access to it very easily and so I feel like more people are just getting themselves involved and this whole conference around just bringing more women into this industry, more people with different backgrounds from minority groups so that we get their thoughts, their opinions into the work is so important and it's becoming a lot easier with all of the technology and tools just being open source, being easier to access, being cheaper and that I feel really hopeful about in this field. That's good, hope is good isn't it? Yes, that's all we need. But yeah, I'm glad to see that like we're working towards that direction and I'm excited to see what lies in the future. We've been talking about numbers of women like percentages of women in technical roles for years and we've seen it hover around 25%. I was looking at some Anita B.org stats from 2022 was just looking at this yesterday and the numbers are going up. I think the number was 27.6% of women in technical roles so we're seeing a growth there especially over pre-pandemic levels. The biggest challenge that still seems to be one of the biggest that remains is attrition. I would love to get your advice on what would you tell your younger self or the previous prior generation in terms of having the confidence and the courage to pursue engineering, pursue data science, pursue a technical role and also stay in that role so you can be one of those females on stage that we saw today. Yeah, that's the goal, right? Up there one day. I think it's really about finding people, other people to lift and mentor and support you and like, you know, we can, I talked to a bunch of people today who just like found this conference through Googling it. You know, and the fact that like organizations like this exist really do help because those are the people who are going to understand the struggles you're going through as a woman in this industry which, you know, can get tough but it gets easier when you have a community to share that with and to support you. And I do want to definitely give a plug to the WIDs at DataIQ. Talk to us about that. Yeah, I was so fortunate to be a WIDs ambassador last year and again this year with DataIQ and I was here last year as well with DataIQ but we have grown the WIDs effort so much over the last two years. So the first year we had two events in New York and also in London, our DataIQ is global so this year we additionally have one in the West Coast like out here in SF and another one in Singapore which is incredible to include all that UTJ team. But what I love is that everyone is really passionate about just getting more women involved in this industry but then also what I find fortunate too at DataIQ is that we have a strong female, just a lot of women. Good. A lot of women working as data scientists, solutions engineer in sales and all across the company who even if they aren't actual data, like doing data work in a day to day they are super involved and excited to get more women in the technical field. And so that's like our empower group internally that hosts events and like I feel like it's a really nice safe space for all of us to speak about the challenges that we encounter and feel like we're not alone in that we have a support system to make it better. So I think from a nutrition standpoint every organization should have like a female ERG to just support one another. Absolutely, there's so much value and a network in the community. I was talking to somebody who been blanking on this maybe in Barcelona last week talking about some stat that showed that a really high percentage like 78% of people couldn't identify a female role model in technology. Of course Sheryl Sandberg's been one of our role models and I thought a lot of people know Sheryl who's leaving or has left and then all the YouTube influencers that have no idea that the CEO of YouTube for years has been a woman. Who has- She came last year to speak at Woods. Did she? Yeah. Oh I missed that. We must have been, we were probably filming. Yeah, I think that's so good to see. But we need more, we need to be and it sounds like Data Iku was doing a great job of this. Tracy, we've talked about this earlier today. We need to see what we can be. Definitely. And it sounds like Data Iku was pioneering that with that ERG program that you talked about and I completely agree with you. That need, that should be a standard program everywhere and women should feel empowered to raise their hand, ask a question or really embrace, I'm interested in engineering. Interested in data science. And maybe there's not a lot of women in classes. That's okay. Be the pioneer, be that next Terrell Sandberg or the CTO of chat GPT, Mira Morati, who's a female. We need more people that we can see and lean into that and embrace it. I think you're going to be one of them. I think so too. Just so that young girls like me, like other who's still in school can see, can look up to you and be like, she's my role model and I want to be like her. And I know that there's someone to listen to me and to support me if I have any questions in this field. So yeah. Yeah, I mean, that's how I feel about literally everyone that I'm surrounded by here. I find that you find role models and people to look up to in every conversation whenever I'm speaking with another woman in tech, because there's a journey that has had to happen for you to get to that place. So it's incredible, this community. It is incredible. Woods is a movement. We're so proud of, at the CTO have been a part of it since the very beginning, since 2015. I've been covering it since 2017. It's always one of my favorite events. It's so inspiring. And it just goes to show the power that data can have, the influence, but also just that we're at the beginning of uncovering so much. Jethlin's been such a pleasure having you on theCUBE. Thank you for sharing your story, sharing with us what Data Aiku was doing and keep going, more power to you girl. We're going to see you up on that stage one of these years. Thank you so much. Thank you guys. Our pleasure. Our pleasure. For our guests and Tracy Zhang, this is Lisa Martin. You're watching theCUBE live at Woods 23. Hashtag embrace equity is this year's International Women's Day theme. Stick around. Our next guest joins us in just a minute.