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Alter Everything

A podcast about data science and analytics culture.
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MaddieJ
Alteryx Community Team
Alteryx Community Team

Do you have ideas for how to benefit your community but you’re unsure where to start? Let data be your guide through the chaos so you can focus on a breakthrough.

 

 


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RADHIKA: 00:00

Hi, my name is Radhika Nath, and I live in Denver, Colorado. I have a cat and a dog, so I'm not a cat or a dog person. I'm a both person. Actually, I'm an animal person. I love all animals. [laughter] And I love people. I love people. I love ideas. I love learning. If you think data science is what you do from 9:00 to 5:00 because you get paid to do it, well, let me tell you a story. [music]

MADDIE: 00:28

Welcome to Alter Everything, a podcast about data science and analytics culture. I'm Maddie Johannsen. And for this episode, I spoke with Radhika Nath, a public policy activist passionate about using data to better her community. Radhika shares why data should be the biggest tool in your kit when tackling daunting societal problems and how underrepresented groups can speak up by telling a story with data. Let's get started. [music]

MADDIE: 01:03

I had first met you at a women in data science panel event at our Broomfield office in March of 2020, which is crazy because it's less than a year ago, but it feels like a long time ago. Right? And I remember walking away from that session with such admiration for you and what you talked about as a panelist. And one of the first things that you said on that panel was data science is not just what you do behind your desk for a living. It's how we're going to change this world. And so I'd love to open up our conversation by diving into this a little bit deeper and talk about what data science means to you, because you mentioned that this sentiment is really what has inspired your passion for data.

RADHIKA: 01:47

This is absolutely correct. If you think data science is what you do from 9:00 to 5:00 because you get paid to do it, well, let me tell you a story. And you can go read this about this guy that I'm going to tell you about. It's a professor. His name is Peter Turchin. Okay? So this man, he was a leading authority on pine beetles. And at some point in his life, he decided, "I've learned everything I need to learn about pine beetles. There's nothing more for me to learn. What could I do that is more interesting?" Okay. So he looks around, and he alights on history. Now, history is just about as unquantitative a field as can be imagined by anyone. If you remember studying history in school, it's all these narratives of what happened here; what happened there. So it's as far as you can get from data science.

RADHIKA: 02:40

And this guy decides that that's what he's going to do. He's going to take history, and he's going to quantify history. And he's done this beautiful, fabulous job of quantifying events. And now, he's able to predict what's going to happen with human society right now. What's going to happen in the future? Can it be predicted? And it's fascinating to see that. So to me, that-- see how he goes from one very esoteric quantitative field into another that's never been quantified, and he brings that lens into it because he's interested in it. And it's nowhere near the field that he grew up in, so to speak. That, I think, is illustrative of that idea that I was bringing to bear, that everything can be quantified. You can learn from everything. And we need to bring that kind of a creative, innovative mentality to our lives and surroundings, yeah.

MADDIE: 03:39

Yeah. I mean, it definitely makes sense. Right? But when I think about the how, how do we adopt analytics, data science, and these statistical concepts and best practices, all of these things that we love to talk about on this podcast and that the Alteryx community loves to talk about, how do we apply those practices to these large societal problems so that we can tackle the big problems that we're facing as humans, like this person did by adopting those best practices, two cross-disciplines, I guess, and answer bigger questions?

RADHIKA: 04:15

Right. So yeah. One of the things that education should do, right, a good education shouldn't necessarily teach you how to think about something. It should teach you how to think because, once you know the rules, you should break the rules. Right? You should try and figure out what is it that makes it work or doesn't make it work. Let me illustrate this a little bit more. One of the things that I often find in youth is this amazing quality of idealism. Right? I remember my youth, and I try to hold onto it a little bit because I think it's such a powerful, powerful thing that, when you're young, you think you could change the world. Right? And I actually believe that you can change the world. So I want to dive into a thought experiment here that-- what is happening really with many people is that we have defined success with a growth mentality because every business, everywhere, we always think about the growth mentality of, "Well, year on year, we need to grow our business 5%. Or we need to grow our membership 2%," or something like that. Right?

RADHIKA: 05:22

And because of that kind of a thinking, when young people are coming out of college, when they're being set up for a future career and stuff, they're looking at very specific things because future is defined as you want to be this person. You want to be eventually on your career path and then becoming a VP or this and that. You want to be a venture capitalist. You want to be successful. And successful often means in a career that takes you into the upper-middle class or something like that. But there are very few routes that we have sort of defined. Go into finance. Go into this. Go into that. I mean, there are very defined fields. So right now, for instance, data science is popular in and of itself. There's artificial learning. There's machine learning. All of these are very, very highly technical, wonderful fields, nothing wrong with them. But I'm going to challenge folks to think differently. Instead of looking up and out for growth, meaning you're looking at somebody else and saying, "That's what I aspire to be." Right? "I want to be Jamie Dimon, or I want to be--" I don't know. Name somebody in the finance industry, "I want to be like that person."

RADHIKA: 06:30

I actually think that real growth, real success, comes when you look down and inwards because you need to know yourself. Right? You need to know what your interests and skills are. And then you need to look down because, in society, we've set up these few paths. And we keep looking up and saying, "I aspire to be like that person," versus you look down and you see, "Oh, my goodness." Look around you. What is happening in society? Oh, my goodness. This is such an interesting problem, garbage on the streets, homelessness. Take your pick. It doesn't have to be a depressing one. Okay? I'm one of those people who always looks at these problems, sort of. But it doesn't have to be a depressing one. And it could be traffic congestion. It can be anything you are so interested in. Right? You could be interested in, "You know what? I'm really interested in universal design and architectural concepts, but in a different way. I want them to apply to - I don't know - the way our city grids work," or something like that. Okay? You can think about things by being very hyper-conscious of your surroundings, of your social milieu that you live in, right, the place that you live in. I'll give you an example again.

RADHIKA: 07:42

Remember that microfinance example? A few years ago, Muhammad Yunus won the Nobel Prize for Peace. This was an economist. Right? Muhammad Yunus was an economist in the University of Dhaka in Bangladesh, and he used to teach at the university. And the surrounding areas, the villages around the university were poor. And he would walk around, and he'd look at these poor people in the villages. And he would wonder, "Well, what do they want to do?" And he would talk to them, and they'd say, "Well, if I had just a little bit more money, I'd have a sewing machine or I'd have this or that." Right? And so he goes to the banks and he says, "Dudes, this is the perfect opportunity. There is a whole swath of the population sitting here just needing a loan. Why won't you just loan them some money? It's a win-win situation. They get some money. You get to have new customers." And the banks laugh and say, "No. They have nothing for collateral. And the loan amounts are so tiny. Why bother?"

RADHIKA: 08:53

And what he figured out was that the average size of a loan was $27. The banks didn't even want these customers. So there's an opportunity. He starts digging into his pocket, and he's taking out $27 at a time. And then he realizes, "Well, I can't help every person. I've got to make this into a system that works." Okay? So the first time he gives out these loans, he creates a system and stuff, and a few people come together. And he gives it out equally to men and women, and he realizes that his rate of return on the loan is 98% with the women and 50% with the men. And he's like--

MADDIE: 09:31

Interesting.

RADHIKA: 09:30

"Well, I am going to stick with the women."

MADDIE: 09:34

Women.

RADHIKA: 09:34

Okay. This is how that microfinance plan that won the Nobel Peace Prize and changed so much about our role happened. Did you know that microfinance now, Muhammad Yunus's Grameen Bank-- it was called Grameen Bank. Grameen is village, the village bank. Did you know Grameen Bank exists in the US? Did you know they're doing--

MADDIE: 09:58

No.

RADHIKA: 09:58

--business here? Yes.

MADDIE: 10:00

No. Interesting.

RADHIKA: 10:00

Yeah. There's a documentary about it. You got to go look it up. But women here get-- the average size of the loan is about $3,000. It's still a microloan because, in this country-- and there's no penalty. There's no collateral. You hold each other accountable. You're in a group of five. The other women get their opportunity because you succeed. And if you fail because life happens, well, you go to the back of the line. You would probably have to prove yourself again before you can come back. But you can't get a loan right away. But that's it. There's no other penalties. Nobody's taking away your car or your home or making you homeless. You've got to give people a chance. Now, look at what he did. He didn't look up and out and say, "I want to go to Harvard and Cornell and do research." What he did was he became hyper-aware of his surroundings and said-- and he didn't do economics necessarily, but he used his economic brain to say, "I'm looking around me. I see a problem."

RADHIKA: 11:01

So let me come back to what my big picture here is, what I want to sort of leave people with. One of the biggest things that people can do, young folks especially, is that instead of focusing on success as something that's up and out again, as I said, I want you to look lower. I want you to bend to lift. You know that weight lifting concept of you bend? You bend before you lift because, in real life, when you bend, you are going to bring yourself down to a strata of society where actually, the problems exist. Then when you lift, you're actually doing meaningful work. I promise you you'll be a happier person. At the end of the day, you'll still be successful. Actually, you'll be successful in an iconoclastic and different way. So when you bend to lift, what you are actually doing is you are actually reimagining what society should look like, what our economy should look like, and what our politics should look like. And those three concept-- let me go into those a little bit.

MADDIE: 12:01

Yeah.

RADHIKA: 12:02

At the end of the day, why are we organized as a society, as humans? It is the most important thing for us. That's why we live together. That's why we work together. Even people who sort of want to have a house in the mountains that is sort of far away, they still need to come down to reality and come into town and everything. Obviously, there will always be some people who are total loners and hermits. But even those people will often crave some human companionship. So the society that we live in is the why of everything we do. Our children need to grow up. Our educational system needs to work. Our transportation needs to work, all of that. Right? Society is why we are organized as we are, and we're working to make it better. If we didn't care, then we wouldn't have street cleaning, then we wouldn't have-- I mean, all of those things exist because we want, as a society, to do better. It's why we do things. The economy is the what. Right? What do we do? Okay. We wanted to have manufacturing. We want to have service. We want to have various things. We want to have inventors and all of that. The economy is what of the work that we do. And the politics, at the end of the day-- I know a lot of people ignore the politics of it. But politics is somewhat of a passion of mine. The politics is how we make it work. Right? There are countries that organize in different ways. But politics at the base of everything is about the allocation of resources to various sectors. Right? So it is just as important as the other two things. And those three things sort of are the stools of a tripod to me, are the legs of a stool, sorry, or are the legs of a tripod to me.

MADDIE: 13:56

Yeah. No. Absolutely. And then, going back to what you're saying about human society is why we do things, the economy is what we do, and then politics is how we do it. When you break it down into those three pillars or those three legs of the tripod, as you say, it helps me kind of wrap my brain around the big problems in the world. Right? However, I do think-- and maybe I'm just projecting here my personal thoughts. But still tackling those large problems, even if we have that framework, it sounds daunting. And I'm wondering if--

RADHIKA: 14:28

It does.

MADDIE: 14:28

--you can share. Right? And so I'm wondering if you can share a little bit more about your use of data in your policy work and how that use of data has really helped you in terms of being able to tangibly advance those improvements that you've worked on in your work in the past in terms of improving your community.

RADHIKA: 14:56

So there are two thoughts that I want to share with you. One is more abstract, and one is more concrete. And let me start with the concrete one because it addresses your question directly.

MADDIE: 15:06

Awesome.

RADHIKA: 15:06

We're just coming off of an election season. So you're asking me about data. And one of the things I did this election season is the-- South Asians. South Asians are people that come from the subcontinent of India, and they belong to the countries of Pakistan, India, Bangladesh, Nepal, Bhutan, Sikkim, Maldives, and Sri Lanka. And I will be happy to take even the Afghanistanis because they are somewhat homogenous with us. And so it's a lovely place if you haven't been to the subcontinent.

MADDIE: 15:40

Yeah. I would love to go.

RADHIKA: 15:42

Right? Right? It is. Actually, there's such diversity, but there's also a certain homogeneity of culture, food, dress. And it's a diversity also in terms of the weather. And the coldest inhabited place on the planet is in India, year-round inhabited. Yeah, yeah.

MADDIE: 16:01

Interesting.

RADHIKA: 16:01

Right? Right? We have also racial diversity in everything. I'm not trying to make it sound all happy, hunky-dory, because human society always has its own issues, and we all want to make improvements. But in the election season, South Asian organizing was something I wanted to take on. So my work was focused around bringing out the South Asian vote. But they don't always show up to vote. And this is actually true of many immigrant communities. The amount of time it takes to go from being an immigrant to becoming a citizen in this country is quite substantial in general. And what happens is, in that time, you get disenfranchised. I voted for the first time when I was almost 41 years old because that's how old I was when I became a citizen, in the US that is. So we have disenfranchised a lot of people so that, by the time they become citizens, they have not had the civic education or the habit of voting. And so that's what I was working on. So we had a really large data set of the entire population of Colorado, and we had to go through that and figure out who are the people that meet our criteria.

RADHIKA: 17:15

And it was sort of a fuzzy matching because we were looking at, based upon names, you could do sort of some other rules and stuff. So it was a very interesting project. So it was one application of data science in real life based on me wanting to work the elections and make sure that we're mobilizing the South Asian vote and bringing them out to vote in this general election. And then, going forward, I can see more such efforts because, as you probably realized from some of the news stories in the mainstream media, immigrant communities are starting to become more active. And activists like myself are trying to make them more active and say that what happens to us and how much power we gain in the system and whether our electeds consider us when making good policy, all that will occur if we start showing up and asking to be considered and to be uplifted and to be elevated. Our issues need to be elevated. So that was an interesting example of that.

MADDIE: 18:14

Yeah, yeah. No, definitely. And I think, pointing out the use of analytics and data science to affect that change, what are some tangible tips for people to improve that data literacy so that way, they can kind of take it with them across any discipline that they want to pursue?

RADHIKA: 18:37

That's such a great question. And let me just take my crack at it. My brain doesn't really work like that. [laughter] I think of data science as another language that you use. I mean, I'm using English here to communicate with you. When I'm with my family, I use a different language. Data skills are another language to try and parse information and to present it back, right, because at the end of the day, what do you use data for? To make better decisions. Right? It's about communication. So for me, I don't start from that end. I don't think of, "How do I apply data science to this problem?" I start with the problem itself, which is what Peter Turchin, the guy whose example I started this podcast with, did. Right? He said, "What am I so interested in? I am done with pine beetles." [laughter] And guess what? He was like, "I love history. I love history. But I can't do what the historians do because that's not how my brain works. Right? So I have to bring my brain to work on the issue that I like." Right? So he looks at history and he says, "What am I going to do with this?" Right?

RADHIKA: 19:53

That's the way you do it. You find the problem. Somebody wrote to me after that talk, and I love hearing from-- there are two people, actually two ladies who wrote to me. And by the way, ladies, shout out to you. Wonderful. Thank you so much. You guys are doing an amazing job. One of them, she talked about the diversity thing that I was talking about. And she said, "I am different. I am very data literate. I am very good at what I do. I also have ASD. And so my brain just works differently. What should I do?" And I was like, "Be you. Just be you." When you are yourself and you're clear about what you're interested in, you'll pursue that. I can't tell you what problems to attack. You will do that by just being authentic and being interested in the world around you. What you do at work you're asked to do anyway, which is great, which is great, because sometimes actually, lots of good stuff comes out of it. I know it has for me many times. It's sort of the interaction of being aware of all the other things that you do and the work and finding meaning in it. Right? There's an intersection that happens when your brain is alive with interest in what you're doing. So when this young lady says that that's one of the things that sets her apart and she's really good at something, I'm like, "Oh, my goodness. I can't wait to see where she goes." Right? And I'll never be able to predict it because who knows? Because we all change, right, Maddie? I mean, are you the exact same person you were in high school? No, because you probably--

MADDIE: 21:29

No.

RADHIKA: 21:29

Right? I know. And that's it. That's growth. And I bet you that maybe there was something of interest back then that you could go back and say, "Oh, my God, I want to do that again." It could be. Right? But the other thing, the other lady that reached out was in India. And she's like, "Well, we're going to use Alteryx in India. Can you get back to me?" And I haven't gotten back to her yet, so I need to get back to her. But I'm actually going to be going to India tomorrow. So when I get there, I'll try and find some time and write to her. But it was also on the Alteryx, the community chat, which, by the way--

MADDIE: 22:00

Oh, cool.

RADHIKA: 22:01

--I tell everybody, "You all need to sign on there because such cool things come out of it," because people talk, and people write to you and say, "This is happening." And I love it. I love hearing from them. And so this young lady's like, "We're doing Alteryx in India. Can you talk to me about it?" So when I get there, I will reach out to her and I'll say, "What are you doing? How are you setting up? This is cool. I mean, Alteryx, yay, [laughter] in India in Bangalore." So I'll talk to her and figure out what she's interested in. But honestly, I mean, this is the beauty of the human brain, Maddie, that you are different from me and that we are complementary. May I go back to that second abstract thought that I haven't covered?

MADDIE: 22:42

Please.

RADHIKA: 22:42

Okay. So that is systems thinking. To me, in my mind, systems thinking is sort of the big, big thing. It's that butterfly effect. It's the chaos theory. It's the system dynamics of the world that everything is sort of interconnected, and little changes can have huge, big repercussions. But people never seem to work from a systems thinking perspective. So when you see problems around you, when you are doing that bend to lift or when you're doing anything, when you're aware of systems and how they work, you realize that the tools that you need for systems thinking often resides in data, that how you set up parameters and everything. So you can start with any problem. And if you tackle it in a systems thinking sort of way, it actually cascades into other things to have good effects. Think about all the ways that we affect change when we make one big change. But the normal tendency, we as humans, we hate change. We don't like change. Change is chaos. Change in little chunks is controllable to us.

RADHIKA: 23:56

And we want that. And that is incrementalism. We like incrementalism. We like to tinker around the edges of stuff and just improve things a little bit at a time. And that actually works really well. It's a very good thing if you're in a good, virtuous cycle, in a good stable system. Okay? But you can have a stable system that's actually not virtuous, that's vicious. Right? And at this point, it's more essential than ever to have a systems thinking sort of perspective. If you're a policymaker, you have to be thinking bigger picture in systems. Now that feels daunting, right, because it's huge changes we're talking about. But that's where data comes in. And data science can sort of be the arbiter there of truth in the sense that, if you can make sure that the data are valid and then show with data how systems work-- because that's big data. That's where normal people, normal policymakers, normal politicians are not able to parse through and understand the longer-term implications of short-term incremental changes versus long-term systems changes.

MADDIE: 25:11

And I think encouraging, as you were saying, from the policy side, thinking big picture and then using data to get there. I can see that totally having a ripple effect across different disciplines. And I want to also tie in what you said earlier about one of your friends who was talking with you about saying, "I think differently. I am different." And your advice was just to be authentic and be themselves and use that to their advantage. I'm wondering if we can kind of combine those two concepts and think about-- we can make sure that the conversation that we're opening up about data is inclusive and really inspiring people. So that way, they do want to get involved. They want to drive the change instead of just sitting with it. Going back to the traffic example, there's a huge traffic jam, and they just deal with it every day. How can we tie those two things together? And also, you yourself, as a woman of color, a parent, just thinking about how can we make sure that people who are historically marginalized are using data to their advantage?

RADHIKA: 26:20

Well, one step before that is-- right now, in my kids' schools and many schools, we are using computers. Right? And we're teaching kids programming. There's a good side to that. Absolutely. Learning anything new is wonderful. But there's also a side where, just because you're doing the same thing that you were doing before and this time you're just using computers to do it, that's not, to me, truly moving towards data science or being more literate or any of that. To me, I want to go back to where I started, which is that a true education allows you to think more freely. Right? And when you think more freely, data is everywhere around us. That's why that guy, that pine beetle guy, went to history, right, because he realized that even history, all those stories were actually quantifiable. Anything is quantifiable. And you can figure out how to quantify things in a unique way. That's what Peter Turchin did. He said, "I can superimpose. I can quantify all of the stuff that is historical narrative. And then I start superimposing society after society on top of each other, and I can start to see a pattern." Oh, my goodness. Right?

RADHIKA: 27:41

I mean, that kind of thinking is what happens if already your mind is free. You're thinking about things that are in your immediate surroundings. By the way, that is an important thing people forget. Just because we are always looking up and out, we forget to look around us. But most of the things that we are most interested in, that we're most familiar with, that we're experts in is our everyday life. It is our everyday life. It is the people that surround us. It is the communities that surround us. It is our daily commute. It is the people we work with. That is what we are all experts in. But we're always looking out and up. So if we were to look around us, we would find ways of marrying that systems thinking and that data literacy piece because, at the end of the day, many people are data literate, and yet, they're not using their full data potential. But you're right. When you talk about data literacy, to me, it comes naturally. It comes naturally. Right? And don't get me wrong. The humanities and a lot of people who are doing qualitative research are also doing amazing, amazing work. Right?

RADHIKA: 28:48

But quantitative science has its own specific ways of understanding, visualizing the world, right, bringing a different perspective that allows us to get a different handle on things. We are able to explain it in a different way to people. And it is not easy for everybody. I will say this because I think our human brains, we like frequencies more than we like percentages, for instance. We like stories more than we like numbers, for instance. So there is a skill to translating data into stories. And that is our challenge because big data, I mean, how esoteric does that sound? [laughter] So many people are like, oh, daunted by it. But honestly, I mean, this is where, I guess, early education, early exposure, realizing, "Okay, I'm using that data to tell a story. How could I tell this in the best way possible?" But you couldn't even make that story without that data, could you?

MADDIE: 29:54

Right. And one thing that you said about looking at your community and how we're all experts in that - we're all experts in our everyday lives - that's such a good point. And with what you're saying about how data really allows you to think freely, I'm curious if you have any stories about ways that data has-- you've really taken notice of the way that data has expanded your mind, or you've really been thoughtful about, "Okay. I'm thinking about data, and now I'm thinking more freely."

RADHIKA: 30:25

I am a person that, in high school, had a math phobia. Yeah. I'm a weirdo. But when I got introduced to statistics, I enjoyed it, and especially measurement theory, research methods, stuff like that. And then along came data science. And my brain always sort of thinks about problem solving. And so there are many people who are very good at data science because - I don't know - they use programs like Python, and they do a lot of heavy-duty programming and coding. The reason I loved Alteryx is because I don't have those skills. I am so bad. When I first took a programming class, when I first came to this country, I took a programming class and-- funny story, but I decided, "Oh, who wants a 100 level course when I could do a 400 level course?" [laughter] Little did I know. So I just went off into some course, and it was humiliating. I did okay at the end. I mean, it wasn't like my worst, worst course. There's another one, contender for that one. But this one, I don't have the kind of brain that looks for, "Oh, I missed a dot here and a comma here or a colon there," that kind of programming. That just wasn't me. So when Alteryx came along and I was able to do all the things that were in my brain without having to first teach my brain to be different, it was so freeing because I was like, "Okay. Now I can actually-- now, actually, I can just fly. I can do what I-- I can ask the data to do what I want it to do without having to actually program." [laughter]

MADDIE: 32:11

Cool. Yeah.

RADHIKA: 32:12

So that was a fun thing, really fun. But the others, it's daily. Right? There are problems all around us. When I was in India, I was looking at how certain areas are really clean and well-kept than other areas. And this is true of our country here too, everywhere in the world. So don't take it personally anybody. But garbage collection is a huge issue. So that's going to be the next thing I'm going to be talking about with some friends who are urban planners and say to them, "How does this work? How does garbage collection work?" So look where I'm starting. I have no data, nothing. Okay? All I'm interested in is garbage collection because I see that there's a difference in different communities and localities. So I'm going to figure out what happens with garbage collection, who does what, and how much are they paying. And why are some communities looking one way versus the other? So that's the end I'll start on. And at some point, I know I will be getting data because I'll need the data in order to quantify the difference. But right now, I'm just keeping an open mind and looking at the problem.

MADDIE: 33:20

That's so cool. And that's a great point. Again, it ties back to what you were saying about knowing your community. And you're going into those communities and asking those questions with those people who are experts. And the urban planners, as you said, it's their job. And they know how to do it. And I have no doubt that once you get into it, that you'll be able to affect the change that you want to. It'll be cool to see what happens with that.

RADHIKA: 33:45

I hope some other young people beat me to it, honestly, because they have more time than I do. But yeah, yeah. It's like the first place where my brain went was, remember when they found those worms that eat plastic? Remember that?

MADDIE: 34:00

No, I don't. I don't remember that.

RADHIKA: 34:02

Yeah. They found a type of worm that eats plastic. You know those plastic bags that are not recyclable?

MADDIE: 34:08

Yeah.

RADHIKA: 34:09

Well, these little worms were lying in those bags or something, and two weeks later, they found all these holes that these worms had poked into it by eating it. I was like, "Wow. Nature has a way." But here's the problem. It takes those worms too long to eat a few holes in a bag. So there are some very cool, neat things that exist. We know about recycling. We also know it's a fiasco in most cases because we've not figured it out. We've not made it a priority. So even garbage, which stinks, is exciting when you're thinking about, "Oh, my goodness. What can we do?" I mean, so it's whatever is around you. You might be interested in something. Just look around you, people. There's something. I bet you, you all can do something different and exciting.

MADDIE: 34:59

Absolutely. Well, one last question that I have for you, Radhika. If you have one piece of advice for our listeners that could be about data, what would that piece of advice be?

RADHIKA: 35:10

I'm not going to make it about data. I will say this, that it will become about data eventually because I know that-- I've met such good people. When I came out there, the kinds of ideas you guys have, data is what you live and breathe. So I'm not going to give you just the data idea. I'm going to say this to you again. Our society right now is in a inflection point where we're really facing some - what do they call them? - big, hairy, audacious goals, those big, big things that have gone wrong or that could easily be fixed if we all had the will to come together and do them. And I'm going to urge you again to look for success, not out and up, but inwards, that you look at people that are doing worse than you in society and try and find ways where you can make a meaningful difference. This is not charity. I'm not talking about you going out and going to a food shelter or something or going and adopting a highway or anything like that. I am really talking about the fact that you look at our society, at what is not working, and look at people less fortunate than you for ideas for success because, to me, you will have lived a much more successful life by doing so. And I promise you, you won't do badly. You won't. You will be just as successful. But it'll be in a more meaningful way. [music]

MADDIE: 36:43

Thanks for listening. Check out our show notes at community.alteryx.com/podcast to find resources and ways to connect with other data enthusiasts. And if you're interested to hear more about how data and policy interact, head on over to Spotify, Apple podcast community, or wherever you listen to podcasts, and search for our new show, Data Science Mixer. In Episode two, we spoke with Alex Engler, Brookings Institution research fellow and data scientist. He walked us through the present and future state of public policy regarding AI and what that means for data scientists. Catch you next time. [music]

 

 


This episode of Alter Everything was produced by Maddie Johannsen (@MaddieJ) and Susan Currie Sivek (@SusanCS)
Special thanks to @jesperwinkelhorst for the theme music track, and @TaraM for our album artwork.