On International Women’s Day, we are reminded of the importance and urgency of this year’s theme which is #EmbraceEquity. Today, we launch Season 2 Episode 1 of Commerce Talk in an incredibly powerful way by celebrating the changemakers, the rule-breakers and the zero-nonsense takers who are shaping and paving the road for a better tomorrow in their own unique ways.
In this very special episode, we sit down with Akanksha Rastogi, the Head of Data and Insights at Foodpanda Thailand. Akanksha is an inspiring speaker and an inquisitor of presumption, who leaves no stone left unturned. Over the last 15 years she has paved the way and has left a significant mark on the world of data-science and business. After receiving her Masters in Economics from the prestigious Delhi School of Economics, she started her professional career working for the world's first customer data science platform, Dunnhumby, where she took her learnings and expertise all around the globe.
In her current role, as Head of Data and Insights and Foodpanda Thailand, she is an advocate for data-backed decision making and evidence-based product development.
In this episode, Akanksha shares some powerful insights about the barriers that women still face today in STEM fields. We speak about the importance of mentors, what turns a good data-scientist into a great one and thoughts on how we can be purposefully and deliberately inclusive moving forward.
You can subscribe and listen to the full episode on Spotify, Apple Podcasts, Google Podcasts, and elsewhere podcasts are found.
You can also check out this Q&A from the episode (edited for clarity and brevity).
Aziza: Let’s get straight into it, what does today mean to you?
Akanksha: I literally stand and speak for the women in the data world, and it's an honour. Let me start by thanking all the numerous first women movers in STEM fields. It's their courage that inspires and enables women like myself to endeavor to do our bit in the space. If you watch the movie Hidden Figures, you know you get broad strokes of the bias and struggles that women face traditionally, whether it be societal expectations, your barrier to access for resources. Our setups are, and you know, race, colour, gender, that sort of increases the barriers for women. Honestly, I love the movie. It's a beautiful, incredibly inspiring movie to watch, but you know, it does sort of give you a picture of how it is more difficult for women to make them work in STEM fields. Now, I'm very cognizant that I stand on the shoulders of giants in the space, and we have come a long, long way. Let's be very honest, data science still lags behind other STEM fields in terms of the ratio of women, and I am really rooting to see this gap go down in the years that come. Of course, I work with very smart, intelligent women every day, and it makes me believe that we are going to get there sooner rather than later.
Aziza: Thank you so much for that incredible introduction and start to this conversation. What do you think are some of the main barriers for women entering this field?
Akanksha: Well, I think one of the primary reasons why we see a low percentage of women in STEM fields is simply because we have an unconscious bias. Even as educated folks, we tend to have these biases when we are even raising our own kids. So, while being well-meaning, we still give that impression to kids that boys like cars and girls like dolls and boys are very good at science and girls are great at art. This is a message that we reinforce in our homes, in our social gatherings, through our toy industry, even at times in schools and universities.
I recall that when I was in university, the seating allocation was so biased for boys, even though we had moved years ahead and we had more representation of girls in the class. But they just expected to have a higher proportion of boys and higher allocation of seats. When you have this general construct where you're constantly reinforcing in subtle ways to girls by saying, “you are going to be better at this” or “your mothers or grandmothers have been so good at this” that distinguishes the spark even before they realize that what they want to do or give anything a fair try.
Now, if I talk about the naturally STEM-inclined women who have, you see that they're either coming from a background of privilege. Their inclinations are nurtured or fueled or they have a mentor in terms of any father, family member, anyone who is constantly encouraging them to question, study and move ahead. Either they come with a combination of incredible conviction and grit against social societal barriers, making them a story of fighting all odds and succeeding. Now what I believe is that while these barriers exist, as more women start joining Data Science fields and proving their mettle, they will inspire more women. So it's going to set off a chain reaction, a positively reinforced change, and we'll see more and more women start to enter this industry.
Aziza: Do you see this chain reaction happening now?
Akanksha: Oh yes, absolutely. Now you start to see so many more women consciously choosing STEM careers, making great strides, and inspiring others. We've started to see a lot more force in terms of women starting to achieve great heights and proving their mettle, inspiring so many others behind them. It’s a topic that we have started talking about in intellectual spaces. We've started recognizing the unique elements that women bring to even STEM fields. There is nothing in how our brains are wired that makes us less successful. As a matter of fact, it makes us probably more likely to succeed in these fields. Now that organizations recognize this, there are programs and scholarships that are helping women break out from these barriers and sort of nurturing talent, and increasingly you see this across the space. So yes, I have big hopes that this will be a change that will be here very soon.
Aziza: How important have mentors been for you in your career?
Akanksha: Mentors have had a phenomenal role in my career. I'd go back and think about who were the people who inspired me as a kid. My dad was a force of nature. I always think that in my teens I very well recognized that I was being raised differently from others, even my cousins. The opportunities,the faith, the challenges that were thrown my way were much different than the stereotypes that were imposed on others. When I got older, my first official mentor, as I call him, Dr. Vishajit Kumar, was very, very helpful. We were working in a space of maternal and neonatal health and looking at how we can make changes and save lives. So it was a huge responsibility on our shoulders, and still, the self-discovery and realizations that I had along that journey have stayed with me for life. I think mentors have a huge role in your life, not only by teaching you but by enabling you to learn. They are people who allow you to have that perspective, and they help you build that self-capacity for growing and learning and succeeding in life.
Aziza: You mentioned stereotypes there, what have been some common stereotypes or misconceptions that you have faced in your career?
Akanksha: I think stereotypes by definition are so perpetuated that they are recognized as stereotypes. If one person believes the thing, it's not a stereotype. You have to have like a critical mass of believers for it to be even classified as a stereotype. As a woman in the data science field, I would say that you do run into a lot of them. I remember when I was younger, much younger and having a career discussion with my manager, and I was pushing him that he should give me more challenging projects. But his response to me was, "You're just going for your wedding leaves in another two weeks. You go do that, come back, and then we'll see if you really want what you're asking me for." And I was like, "Excuse me? Me getting married will not change my mind or my ambition." Again, if we talk about maternity leave, a lot of the managers in Data Science Fields sort of view it as a vacation because, you know, these are men who have probably not ever changed a diaper or woken up at midnight. You expect that the person right in front of you is going to have a nice relaxed time. You do not understand that they need that time to be able to get used to being a new mom, to take care of their child, to figure out their new life balance. And so, I think, there are these stereotypes that women are not as ambitious or they're going to leave soon or they're not as capable as men in certain fields, and I think it's really important to challenge them.
I think that we need to change the way we view parenthood as well. It's not just the mom's job to take care of the child. It's a shared responsibility, and we need to support that, both in terms of policy and culture. As companies, we need to be more mindful of the fact that women have different needs at different points in their lives, and we need to create an environment where they can thrive regardless of where they are in their personal lives. That’s just good business sense. Because if you have a diverse and inclusive workplace, you're going to have better ideas, better innovation, better solutions, better products, better everything.
Aziza: I look at your work and I see you're an advocate for design thinking and using that within the methods of your work that you do, and at the center of design thinking is empathy. How important is empathy in your role?
Akanksha: Well, I think empathy is not one of the fundamental skills that makes you a good data scientist. But what it helps you do is become from a good data scientist to a brilliant one. "Why? Because when we're looking at data, we have a host of techniques and methods and packages and languages to code out the problem, solve it, extract the right information. But, you know, just that information or data is not powerful per se. Because that's where we talk about mindless reporting, where you're constantly just reporting, but the other person doesn't know what to do with it. Data insights become powerful when they get translated into actions and strategies. To translate it into action and strategies, you need to tell me more than, 'this is what happened.' You need to tell me why this happened, whether it is likely to happen again, what might these groups of people be thinking, when they take this kind of action, why? Because then I'm going beyond what I learned historically and I’m trying to think about what are the drivers of this behavior and then think about what if I change the construct a little, change the incentives a little, change the flow a little. What do I expect these people to do? If you can't get those insights and build those hypotheses, you can't hit the next cycle. The next stage right now, that is what brings real power. And if you don't have empathy, if you can't think of why would this person do this thing, it's very, very difficult to unleash the next round of iterations, testing, hypothesis building, or product improvements. So that's where I believe that empathy makes a key, it's a key differentiator between a good data science practitioner and a brilliant one.
Aziza: On becoming a brilliant data scientist, what has been the best piece of advice you've ever been given?
Akanksha: Well, you know, there has been a lot of advice that I've been given that has left a mark on me. I still go back and think about the advice on days when things are not going my way. My first mentor told me that, “Confusion precedes conviction, life is almost like a game of dots…” While you have to aim to hit the bull's eye, what is very important is that each throw is teaching you something about the conditions, the ecosystem that you're in, and it's giving you a very tiny hint how to get closer to your goal. If you tug at that hint, that's what's going to give you a little more clarity. Don't aim to know everything before you start. Just be sure that this is the direction you want to move in until you reach your destination. If you obsess with being right on the first go you will sort of lose focus from learning from your previous mistakes, your previous attempts and. Improving and moving faster. What you need to do is fail fast and learn fast.
Aziza: On that note, thank you so much, Akanksha, for joining us. And if there's anything that you'd like to leave with our listeners before you do so, please do.
Akanksha: I'm really, really touched by you guys giving me the opportunity to come here to talk about data science and talk about how women have made a difference and a mark in this field, and what else needs to be done. Data science is a beautiful upcoming space. We're always looking for great talent and people to make a difference. Just when you come on board, be kind to the next person sitting beside you because they might have different challenges on their mind, but that does not make them less capable or less likely to succeed. So much that this industry we're based on, you know, sharing an open knowledge base and everyone doing their bits, right? We can achieve so much by being an inclusive community by recognizing people's talents and limitations, and we would want this space to be one of the most inclusive places. So let's maybe keep our ego a little bit aside and give the person next door a chance to prove themselves. That's my only message and happy international women's day.