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Episode 9

Global Connectivity: Share and Democratize Through Open Data

Stanford Professor and Co-Founder / Director of WiDS, Margot Gerritsen joins Sam Ramji in a conversation about how a data community provides global connectivity, and how learning is all about seeking discomfort with uncertainty and ambiguity. Learn how data is the new gold, but rather than sitting on the mine - share the wealth through a career in Data Science.

 ·  Register for the next WiDS Worldwide Initiative, happening March 8.
 ·  Email Margot with any questions at margot.gerritsen@stanford.edu

Published January 14th, 2021  |  Runtime

Episode Guest

Margot Gerritsen

Margot Gerritsen

Stanford Professor and Co-Founder / Director of WiDS

Episode Transcript

Margot Gerritsen:
If you are in this field of data science, it's very likely that you're constantly promoted away from your comfort zone. And so what you have to create is what you said, you have to become super comfortable with being uncomfortable, and that took me a while. In fact, now, to be honest, I feel a little uncomfortable when I'm too comfortable, and the reason for that is that I know if I'm super comfortable, I'm not learning.

Sam Ramji:
Hi, this is Sam Ramji, and you're listening to Open||Source||Data. Margot Gerritsen is a Professor of Energy Resources Engineering and Computational Mathematics at Stanford University, and she's also the former Director of the Stanford Institute for Computational and Mathematical Engineering. Originally from the Netherlands, Margot earned a master's degree at Delft University of Technology, and in 1996, received her doctorate at Stanford in Scientific Computing and Computational Mathematics. Margot is also the co-founder and co-director of the Global Women in Data Science Initiative, which includes hosting the Women in Data Science Podcast, a series of discussions around both technical and personal journeys, creating an, "Of course, we can" culture. Margot, welcome to the podcast.

Margot Gerritsen:
Oh, thank you so much, Sam, and thank you for pronouncing my last name just the way we Dutchies would do it. Well, it's a pleasure to be here.

Sam Ramji:
Awesome. Well, I'd like to start each episode asking our guests, what does open source data mean to you?

Margot Gerritsen:
And I love that question. So, for me, open source data stands for not just the data, but also associated software and tools. It stands for a lot of what is positive and really quite fantastic in this crazy world of data science that we're in right now. We spend a lot of time also in lids and on campus thinking about the negative aspect of this data driven world that we're in. But one of the wonderful things is that there is a possibility for us to really share and to some extent democratize data science through open source and open data. So, for me, it really is, apart from the community that we can build, which is one of the things we're trying to do in Women in Data Science, open data and open tools form this other very positive aspect of this world. And here is one of the reasons why I say this.

Margot Gerritsen:
I spent about 20 years of my career applying data tools, but also simulation tools to energy, and when you think about energy resources, there are the haves and the have-nots in the world. And the reason is that you have to be in a particular geographical location, you have to be a little lucky as a country or as a region that you're sitting on the resource, either on a solar resource or a wind resource, or, of course, as it has been for a long time, oil and gas. And with that comes money, with that comes influence, and with that comes power. But now we are in this era of data and people say sometimes that data is the new oil or the new bacon or the new, whatever, the new gold, whatever you want to call it. And here is the interesting thing, that is not geographically really restrictive. That can be shared. Everybody can. Of course, I always have to put some constraints on this.

Margot Gerritsen:
But when you think about it, you need very little. You don't need to sit on the goldmine. You don't need to sit on top of an oil reservoir. What you need is storage, computing power, and education, and that is much more available openly. And so I find this one of the most exciting things about this world that we're in, that we have maybe through this data-driven decision making that we're seeing right now, and this wealth that is being created through data science, including AI, machine learning. I throw everything in there. That wealth, maybe you may be able to distribute that much more widely. And now, of course, for that we must share, and that's why I'm a big fan of open data, open tools, as well as open education and free education. That is what drives me, particularly in this area.

Sam Ramji:
Just awesome. There are so many things to delve into there, so many threads to pull, and I think we'll probably spend the rest of our conversation pulling those. One of the things that you've done and that you pointed out that open source is traditionally built on is building communities, and so the Women in Data Science community is a breakthrough in being able to create a distributed worldwide community of women focused on advancing the practice, the art, themselves, the openness of conversation, mutual support around data science. And you've just announced that registration is open for the 2021 WiDS Conference, and I think you've also opened the Datathon last week.

Margot Gerritsen:
Yes.

Sam Ramji:
Can you talk about that for just a moment so our audience can find out more?

Margot Gerritsen:
Yeah, it's a very exciting week for us and for those of us in the U.S. I'm super happy we had this week because it makes me feel just a little bit more positive and optimistic. But today, we opened registration for our March 8th worldwide conference. That's a 24-hour conference, virtual, where we go around the world listening and talking to outstanding women doing really fantastic work in data science all over the world, which I'm really very excited about. We start at Stanford with our central conference here, a two hour segment. Then we move to the Asia Pacific region. We spent about six, seven hours there. Then we move to Europe and the Middle East and Africa, and then back to the Americas. And we end with a closing ceremony focused around Hawaii. So it will all be really exciting. And we're hoping to draw thousands and thousands of women and men and people from all genders, really, to that conference. And then last week we started our Datathon, and that's really growing. It's usually an event that lasts a couple of months where teams can register from all over the world. In fact, we have a lot of cross country teams as well, and people hook up through our platform as well, where they work on a data challenge. This is in collaboration with Kaggle. And then at WiDS, at the big event on March 8th. We will let everybody know who are the winners.

Sam Ramji:
That is tremendously exciting. The Kaggle is a neat community, too, speaking of them. I remember getting a chance to meet Anthony Goldbloom. I happened to be at Google when Kaggle joined Google as a corporate acquisition. Just a really thoughtful, charming human being, and is representative of a fairly amazing open data community.

Margot Gerritsen:
Yes. And we have loved working with Kaggle, and one of the things that we do and that Kaggle also really likes is to ask the teams to be at least 50% female or female-identifying. For Kaggle, that has also meant that many more girls and women are exposed to Kaggle, because before we started collaborating with them, the percentage of girls and women participating in Kaggle competitions really wasn't all that high. And so we see a lot of first-timers now go through Kaggle. And the nice thing is, is that these people tend to stay engaged. So I think that is just fantastic.

Sam Ramji:
That's something that brings together a bunch of the things that you've done first as an international person, starting in the Netherlands and coming here, as well as the current level of technology we have to be able to do global collaboration. This idea of community building and transcending boundaries is something that you're doing I think in a couple of dimensions. One is, how can you help people move past the boundaries that have been set for them? Can they go do things, get jobs, do work and do research that they were told they couldn't? But also how do we cross these boundaries of time and space with data? How do we move towards a place where we can share the data, we can visualize it? What stands out for you about the work that you've done in the last few years as you've seen this community grow, as you've nurtured it, that makes you the most curious or maybe the happiest?

Margot Gerritsen:
The happiest is easy, Sam. We have seen through WIDS so many women and girls from all over the world get very encouraged and excited with the support that they get through the WiDS network.

Margot Gerritsen:
Most women that work in data science around the world, they probably start off thinking they're the exception rather than the rule, right? That was certainly the case for me in computational mathematics. Yes, I knew some women, but you're very, very often the only woman in the room of men. Or 10% women at a conference, for example. Or the only female speaker at the conference, which happened to me many times in the past. And that gives you a sense of you really feel different and it gives you a sense of isolation, a sense of not really belonging. And then, of course, it's very natural for women to then say maybe it's me. And it's no surprise to me that a lot of the women and young women that we talk to feel a bit like an imposter.

Margot Gerritsen:
And they say, "Well, I'm not really feeling a part of this culture. It must be that because I don't really belong and I'm not contributing." So what we wanted to do with WiDS is to show girls and women everywhere that they're not alone, that there are many, many thousands of probably millions of outstanding women doing outstanding work all around the world, and that they recognized that and that they can see such women and that they can see such movement in their own region. And so, we provided a way for women to organize that way, to find each other.

Margot Gerritsen:
Let me give you one example that just made me unbelievably happy. When we started our global women in data science, we got contacted by two women from Bolivia and La Paz. And in our first conversations with them, they said, "We want to have a WiDS event in La Paz. And we think there are four data scientists in Bolivia or La Paz that are women. We know four. And they organized that day and they had 50, 60, maybe 100. The interesting thing was that afterwards, they said we did not even know. So even in our own country, we did not know there were more. And they come out of the woodworks and that's an incredibly powerful feeling. We've had letters from girls from small towns or villages in India who find us and we always live stream free. It's freely accessible to anybody, all you need is a cell phone, really, a smartphone to get through to us.

Margot Gerritsen:
And we have girls say, wow, there's Indian women doing that. This is an area that I didn't realize there were so many women and they felt empowered to then continue on. We've seen the same in Japan. We've seen it in the Middle East. We're in over 70 countries, including countries where you think the women wouldn't be active, maybe Saudi Arabia. Maybe you don't think about, we're very active for Saudi Arabia. So that has been unbelievably inspirational to me.

Margot Gerritsen:
One of the most interesting things for me also has been that there are still, despite the fact that we've been going on now for five years, and despite the fact that for as long as I can remember... So from the time, in say the early 80s, when I entered this area of computational mathematics and scientific computing, data science is really part of that umbrella area of research. There's always been this talk about women needing to break through. We've had maybe 10% to 15% women for decades now in this field. And there's always been this sense of, well, for conference organizers, for example, that they cannot find the female speakers that are good enough, that women are not being promoted.

Margot Gerritsen:
And that in order to break through this and change, these women have to lean in. I'm using Sheryl Sandberg's words here on purpose, meaning that they really need to take the ownership of changing the culture. Right? So here's what we're often told. It's a man's world right now. It's okay. As a woman, you can enter this if you adjust. Once you're in that culture, you can change it from within. So basically, it puts the onus on the women and then we will affect change. Well, I've heard this for nearly 40 years now. And nothing's really changing. I don't believe that. I think that the cultural change that needs to be seen can be accomplished if we have a critical mass of women. So the research points out it's about 25% to 30% that you need before a minority stops being seen as different and really becomes part of the culture.

Margot Gerritsen:
And before we get to that, the responsibility or the ownership of that really needs to sit with the majority, not with the minority. So to me, that's been super interesting, Sam, is that seeing this now so clearly through the WiDS community, that it is still the same everywhere, despite more than 40, 50 years of affirmative action of girls in STEM or all sorts of activities that people have organized. We're still not there. I think what the power of some things like WiDS, or Women in Data Science, is that it is very clear at these events that with a different language and a different culture, we can be working and collaborating at a really high technical level.

Margot Gerritsen:
These are technical conferences that we have, and it becomes clear also to the men that come that this sort of standard more male-dominated culture, that has particular characteristic, is actually not necessary for success. And that is one of the problems is that I think a lot of people resist changing culture because they feel that that change in culture may not drive innovation or success as much as this alternative, maybe more open and collaborative culture would. And that's just not the case. And I think people are seeing this gradually.

Sam Ramji:
Yeah, it's almost a cargo cult mentality, but one of the things that's super interesting about the community that you created and what you're describing is that openness and safety and collaboration to share language. And so much of what we need to understand in a new space, like data science, is new language, the challenging and important work that the community is doing to create new language around types of visualization. Things like how do we advance from literacy to starting to focus on numeracy? How might we pair all of these constructs to create a better democratic base for how we behave as citizens kind of all relates in a pretty neat way. I think you're exploring a lot of new core collaborative constructs and ways to understand the world through these tools of data science. I'm curious to get your thoughts on this and see what you see in the year ahead.

Margot Gerritsen:
I mentioned that one of the challenges we have nowadays is that it's so easy for people to find exactly the information that confirms their own preconceived ideas. You have this very strong confirmation bias in everything that's going on. A political atmosphere where people can find the echo chambers that they're most comfortable in. And the same of course also is in research. And this is one of the reasons why I'm so keen on diversity in the workplace and diversity of thought and of culture. And of course, that includes diversity in genders, simply because it is very easy to get trapped into an approach to work and the decision making process or a data analysis process that is completely biased.

Margot Gerritsen:
What we've seen in teams that are more diverse and in communities that are more collaborative is that that is really no longer possible. And that's uncomfortable for a lot of people. So it's logical that people... And I'm not criticizing. I'm criticizing people for doing it, but it doesn't mean I don't understand them. It's much more comfortable to be with like people often, to hear confirmed what you already thought. That's very nice. It's very uncomfortable. Even for students. We see this when they're working on their Ph.D. research, for example, it's very uncomfortable to get pushback on your ideas, to be challenged on your findings. That all makes us... Yeah, everybody immediately feels like, oh, my goodness, I'm being judged.

Margot Gerritsen:
I'm failing." Much more comfortable to just get confirmation, much less interesting because then you're not really learning. I often think about this because I've been teaching at university for 35 years or so, nearly 40 years, and over the years, students have become much less capable to deal with that sort of pushback and to deal with that discomfort. They're seeking more and more this constant confirmation that they're okay and they have a really hard time dealing with failure much more so than before.

Margot Gerritsen:
That's another topic of discussion, but you see this in the workforce, too. When you go to a conference and you challenge somebody on data analysis, they get very, very defensive. That comes from a place of fear and discomfort. I think we are not that used, maybe anymore, to being uncomfortable. When we go through high school, for example, it's all about the grades. We all want to get a 100% on everything and A+. We also see in education there's very little ambiguity. You either do it right or you do it wrong. When you then get into research, where there's an incredible amount of ambiguity and there is no right way, there is no one answer, there is no linear path to go from your question to your answer, so everything that you've learned in high school has to be unlearned.

Margot Gerritsen:
One of the biggest things you have to learn is to feel comfortable with uncertainty and to feel comfortable with ambiguity. You see this, of course, also in political behavior. You see it in so many aspects of life. It comes from place of fear and this fear of failure, this fear of disappointing, this fear of being left out, this fear of being different, maybe not being good enough and general fear, of course, of not being totally in control of our own lives in the political sphere because of everything that's going on and the unbelievable over information that we get.

Margot Gerritsen:
I really feel for my son, a young adult, and his friends, and all of the students that are coming through because when I was their age, I was not overwhelmed every single day with so much data. I had to read through all of this by myself. This overwhelm is very upsetting and it makes you fearful because it makes you feel extremely vulnerable that things can happen every day and they do. You hear about them and when people get fearful, they get very selective in what they listen to. They tend to listen to the things that comfortable, and comforting, and close to their own thoughts.

Sam Ramji:
It's really interesting to get your longitudinal sense of shift in students' sense of background fear and how that inhibits their curiosity, right, or their comfort in uncertainty. I grew up in the Netherlands in the 80s during the Cold War and I think that we felt less fear than what I see in our kids today. I think one of the things that's really generating a lot of fears, sort of an existential fear of, "What do I do after school?" My daughter's just about to graduate from the great rival of Stanford from the University of California at Berkeley. I don't take the rivalry seriously, but of course, the universities do, but she's very interested in data science.

Margot Gerritsen:
I love Berkeley.

Sam Ramji:
Yeah, they're great schools, but she's very interested in data science. At the same time, she doesn't know what to do or where the jobs are. I think one thing that folks who are listening to us would be fascinated to get a sense from you is what should they expect when making a career for the set of students that are graduating into this global crisis? What could they focus on in the near future? How might they prepare themselves?

Margot Gerritsen:
Wonderful question, Sam, and one that I get so often from students. Looking, myself, at this world out there, I'm often surprised that students are so concerned about the opportunities in this field are unbelievable. In fact, in a time of crisis, the opportunities are maybe even bigger. There's so much that can be done. One of the reasons I think that students have this fear is because, again, they are now given so much in cookie-cut form. I see a lot of students, say, but for example, that want to go in academia, so they say, "This is what I must do to get a professorship in academia." There's a formula for everything, and they feel that if you want to go from A to B, there's a certain prescribed path and there's a certain set of requirements that they have to meet.

Margot Gerritsen:
I think when I graduated, when you graduated, and with our generation, we didn't get that at high school or at university. It was a lot more ambiguous. We had to sort things out for ourselves and it was the same with careers. We were never told, "Well, this is the best CV, or this is what you have to have to get into a top school." No, it was much less recipe-driven, but now, maybe because there are so many people that have a soapbox out there and they can claim those things, everything seems to be, "This is the profile of somebody who's going to be able to do this and I don't fit the profile. What am I going to do?"

Margot Gerritsen:
The truth of the matter, of course, is that there are so many different opportunities for so many different people with different backgrounds. I've seen folks who are quite courageous and are willing to dive into something new with a degree of education become fantastic data scientists. The thing is really be out there and connect with folks and talk to people. The trick is really to find these communities and this is, in fact, one of the things we want to do with WiDS is to provide these communities to women, young women, like your daughter who can then find role models elsewhere and can see all these different career paths that people have had through the podcast, through all the speakers that we'd send through the community that we've created.

Margot Gerritsen:
That is the best piece of advice that I can give to anyone looking for a job is to find this community. You can find it with WiDS, but you can also find it with meetups. The women and machine learning and AI meet up is fantastic. We've collaborated with them. That's a really great way to do it, and of course, other meetups also with mixed groups, right? I've seen a lot of students get into companies by doing internships, and of course, then you just push the problem back a little bit. How do you get those internships? Again, this is mostly with networking.

Margot Gerritsen:
A lot of people from universities, they get these networks through alumni, so alumni of the department they're in or not. I actually find it amazing to see how reluctant students are to reach out to alumni. Even in ICME, this Institute that I was leading, we made it relatively easy. We would connect the students to the alumni and still the number of students that would proactively reach out is very low. When you ask them, "Why are you not talking to people? Why are you not connecting?" Very often I get back, well, that, "I'm too nervous to do this because they may reject me." There is this real fear, I think, of rejections, of not quite having what it takes, of feeling inadequate, of feeling like an imposter.

Margot Gerritsen:
One of the big things, of course, for everybody to remember is that when you are done with your studies, you don't have a hundred percent of the skill set that somebody is looking for, but they're not going to hire you for that anyway. They're going to hire you because you are self-learner, because you are agile, you have both the desire, and the passion, and the enthusiasm, but also the foundational skills. That's what your degree is for, to quickly pick up new tools and new skill sets. You have the eagerness to learn and to apply yourself in different ways. You have the courage also to do that, to jump into something new and feel like you're an apprentice, but then learn as you go. That's what people are really looking for.

Margot Gerritsen:
This is what I tell the students and it's completely opposite to how students come into college. They come into college with this idea. There is a certain skill set I must have. There's a certain number of points on my CV that I must have and if the unit, or company, or department advertises for a particular person, I must meet every one of those points.

Margot Gerritsen:
Otherwise, I'm not good enough, and that's not the case. To become agile you have to be a real learner, you have to believe in this growth mindset. You have to have this passion of learning. There's a passion of learning, and be comfortable with learning, and a lot of people are not. They're very uncomfortable with learning, because as you start learning, particularly when you get onto a steep learning curve, in the beginning of this learning curve you feel like a total failure.

Sam Ramji:
Mm-hmm.

Margot Gerritsen:
There is so much you don't know. You have this huge hill to climb. I always think about this metaphor of the rock climber, those people they get really excited when they see a big high mountain to climb on. I mean, that is the fun part is to actually climb the darn thing, and making them come back to these mountains. That's what you want to get to as a student, that every single time there's something new. "Oh, but this is exciting, I'm learning." How privileged we are if at the end of the day we can look back on a day and can say, "I learned this today or that today. How freaking awesome is that?"

Sam Ramji:
Just awesome advice. We are absolutely looking to employ people who are curious, and courageous, and that journey is so challenging because you step onto it and you're consciously incompetent. What you hope to become is unconsciously competent, but that's a long, long road.

Margot Gerritsen:
Yeah.

Sam Ramji:
You have to have your ...

Margot Gerritsen:
That's right.

Sam Ramji:
... curiosity and your courage in each hand.

Margot Gerritsen:
The thing is also what people want then is get this confirmation that they're competent from others, and the thing is after high school, and maybe undergrad, where this confirmation it's false confirmation, or false reassurance to some extent. It comes with assignments. it comes with little projects, and then exam. You get a certain grade, you think, "Oh, I have an A, now I'm competent." Of course, that's not really always a great measure. The older you are the less often will you get that positive feedback, will people say, "Oh, you've got what it takes."

Margot Gerritsen:
What's much more likely is that you constantly, every single time you master something, you're put into something you don't master again, so, you're always entering this new phase. Just at the time when you think, "Oh, this team leader stuff, I've got this now. I'm a great team leader," well they make you manager. Well, there you go you're stretched again and out of your comfort zone. Whenever you're good at coding in something they say, "Oh, you know how to do Python well, we need somebody who builds this in Julia. Why don't you go do that?"

Margot Gerritsen:
So, if you are in this field of data science it's very likely that you're constantly promoted away from your comfort zone, and you're constantly promoted into a zone of discomfort. So, what you have to create is what you said, you have to become super comfortable with being uncomfortable. That took me a while. In fact, now, to be honest, I feel a little uncomfortable when I'm too comfortable. The reason for that is that I know if I'm super comfortable I'm not learning. If I'm not at least a little bit nervous from time to time about giving a talk or giving a new class then I know I'm not really stretching myself, and it's addicting. When you get to that stage it's addicting, it's addicting to say, "Oh, there's something I can learn."

Margot Gerritsen:
So what I do is whenever I want to really learn something, I volunteer to teach it, and then I think to myself, "What have I done? Why am I doing these things?" Then I panic for a while and I think, "Oh, why do I keep challenging myself?" But it is show accelerating because then I have learned something and I'm an unbelievably lucky person in that I can make a living by learning. I think in data science, if you ask people what is really fun for you? Apart from for some people say, "Oh, the salary I'm making," but for most people the fun part is that learning.

Sam Ramji:
That is a fantastic note to close our conversation on making a living by learning. I think it was incredibly inspiring. Margot, thank you for your generosity of spirit, your generosity of mind for taking the time with us today, and thank you for what you've created in WiDS. I think it's something that all of us can look to and learn from in raw community building. I think it's given many people, I know women, transgender women, genderqueer folks, a place to connect and to aspire to be better.

Margot Gerritsen:
Everybody's welcome. So please join us.

Sam Ramji:
Fantastic. Well, thank you so much.

Margot Gerritsen:
Thank you.

Sam Ramji:
We'll put all the links and all the information alongside the podcast, and look forward to talking with you again I hope.

Margot Gerritsen:
That's wonderful, and if anybody wants to reach out, put my email there, too. I always answer emails.

Narrator:
Thank you so much for tuning in to today's episode of the Open||Source||Data Podcast, hosted by DataStax Chief Strategy Officer, Sam Ramji. We're privileged and excited to feature many more guests who will share their perspectives on the future of software, so please stay tuned. If you haven't already done so, subscribe to the series to be notified when a new conversation is released, and feel free to drop us any questions or feedback at opensourcedata@datastax.com.