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Alter Everything

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

From sudoku-obsessed to becoming actuarial analysts, Alteryx ACE Kenda Sanderson hosts a chat with colleague Erin Wagner to dive into how math, puzzles and data blew their minds from an early age.

 

 


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Transcript

 

Spoiler

MADDIE: 00:00

Welcome to Alter Everything, a podcast about data science and analytics culture. I'm Maddie Johannsen, and today I passed my hosting mike to one of our ACEs, Kenda Sanderson. You'll probably recognize Kenda, as she was the guest host from our last episode.

KENDA: 00:14

One Christmas, I got an electronic version of sudoku--

ERIN: 00:17

Oh, how fancy.

KENDA: 00:19

--and I would carry that with me to the grocery store, to holidays, family holidays. When my brother would be playing his Gameboy, I'd just have my little sudoku electronic game out, playing that.

MADDIE: 00:32

Kenda is an actuary at MedPro Group, and she invited her colleague and fellow actuary, Erin Campbell Wagner, to be her guest.

ERIN: 00:40

So I remember just making up these crazy, complicated math problems that I would solve on the board, like 10,384 divided by 7. And I would practice my long division.

KENDA: 00:53

Typical kid things.

ERIN: 00:54

Oh yeah. The fact that that was how I chose to spend my time is probably indicative of my nerdiness.

MADDIE: 01:02

From finding inspiration in sudoku puzzle books and a Who Wants to Be a Millionaire game show question, Kenda and Erin dive into how math and data have blown their minds from an early age and how those revelations turned into their careers today. Here's Kenda.

KENDA: 01:20

[music] Hello and welcome, everyone. My name is Kenda Sanderson, and I have taken over the show today for a very special episode. Real quickly, to give you some background on myself, I am one of the Alteryx ACEs. You can find me as Kenda on the Alteryx community. I work for the insurance industry and I've been using Alteryx for just over five years. I have a good colleague and friend of mine here with me who's going to chat all about math and data and why they're so cool. As Alteryx users, obviously we're big fans of data, especially when it's clean. On top of that, as actuaries, math and data are very important aspects of our job. So I'm sure that it's no surprise that this topic is very near and dear to our hearts. So join us as we geek out for a little bit and talk about why this stuff is so exciting. First, let's get to know Erin a little better. Hi, Erin.

ERIN: 02:10

Hello.

KENDA: 02:12

Welcome, and thank you for joining me. To start off, can you give us a quick introduction on yourself and a little bit of your background?

ERIN: 02:19

Absolutely. My name is Erin Campbell Wagner, and I am an actuary. When people ask me what I do, more often than not, they have no idea what an actuary is. So I'll just quickly explain what that is for those of you who've never heard of it before. Actuaries are in the business of risk management and analysis. So we have the really fun task of trying to predict the future using current data. And what I currently do as an actuary for MedPro Group is pricing insurance for senior living centers and hospitals. As you can imagine, this is a pretty complicated job, but we have identified some good variables that we can use that are indicators for the riskiness of a particular facility that we write. So in a nutshell, that's what actuaries do for insurance companies, is try to establish what an appropriate rate would be for insurance. And I've been doing this for almost nine years. I've worked for three different companies over the course of my career. And I started out working on homeowners and auto insurance and then moved into general liability, which is insurance for businesses. It would be insurance for if someone were to slip and fall in a restaurant or were to injure themselves at work. Then I moved into medical malpractice, which is the insurance that I am working on for MedPro Group. So I've seen a wide array of different insurance products, and they're all fun and exciting in their own way.

KENDA: 03:53

Great. I'm actually really glad that you took the time to explain that because like you, often when I tell people that I'm an actuary, they usually just sum it up by saying, "math." And so sometimes I forget that it's not a very widely-known profession because we do it every day. But in the real world, it's not something that is maybe as common or as well known as maybe a teacher or something like that, that everyone knows what a teacher is. So while it's true that we have some math tricks up our sleeves, data in general plays a big role in what we do as well. And when you think of careers and data, many people may think of maybe a consultant or a data engineer or a data scientist. But as actuaries and, obviously, users of Alteryx, our use of data is growing. So to start off the discussion, I wanted to kind of throw it back to the very beginning. Thinking back, what career dreams did you have as a kid?

ERIN: 04:54

Yes. I started out at a very young age. Well, I kind of jumped around career interests a bit. I wanted to be a baker for a while because who wouldn't want to just bake beautiful cakes for a living? And I thought meteorology was cool. But I think the main career aspiration I had as a young child was being a piano teacher. I started playing piano at the age of seven or eight and I loved it. And I thought that's what I was going to do with my life. But then in middle school - I think it was around sixth grade - math started to become more interesting to me. I had always enjoyed it, but sixth grade was when we started doing some kind of rudimentary algebra and some real basic geometry, like learning how to calculate the area of a triangle. Stuff like that. And it became much more interesting to me. And then I started thinking, "Oh, maybe I want to do something with math for my career." So I remember there was a project we had to do in seventh grade where we had to interview some people in the community who had jobs that we might want to do when we grow up. So I had thought about being a math teacher and maybe an accountant. I had spoken with the women who did the bookkeeping for our school and learned a little bit about accounting. And so those were the two careers that I was really seriously considering. And like you mentioned, Kenda, everyone's heard of an accountant, and everyone has obviously encountered a math teacher because we all have to learn it. So those were the things that were most accessible to me and the most obvious to me, being interested in doing something with numbers.

ERIN: 06:30

But then when I got into high school-- the way I heard about actuarial science is kind of a funny story, so I'll tell it. I was watching Who Wants to Be a Millionaire with Regis Philbin, just the classic original--

KENDA: 06:41

The original version.

ERIN: 06:42

Yes. [laughter] And I loved that show. I had, at one point, thought I wanted to be on that show as a contestant. And the individual playing the game-- it was, I think, maybe the $16,000 question. And it was something to the effect of, "What person calculates risk for a living?" And then it had the four options and one of them was actuary. I, of course, had never heard of actuary. But what sparked my interest was this whole "calculate risk" thing because I found probability to be very interesting at that time, and statistics was also interesting to me. So this "calculate risk" thing was like, "Oh, that sounds exciting." And the guy who was the contestant on the show, he was like, "Oh." He chuckles, and he's like, "I know the answer to this because I'm an actuary." Just this totally serendipitous thing. So, of course, he gets the question right, and Regis Philbin gets quite a kick out of the fact that he does that for a living. But I was like, "Oh, this is interesting." So I run over to the computer and I Google "actuary." And I just start reading about it more. And the more and more that I researched it, I was like, "I think this is the career for me. This really is the type of math that I'm most interested in." I really was fascinated by the prospects of trying to predict the future, which is obviously impossible. But to whatever extent it can be done, I wanted to try to do that, so. So that's how I latched on to actuarial science and I just kind of stuck with it since then, and in college started taking the exams, and, well, the rest is history.

KENDA: 08:21

The next closest thing to being able to read palms, right?

ERIN: 08:25

Yes. [laughter]

KENDA: 08:27

And you know what's funny is, I would say I actually had a very similar in a way. Although I didn't watch that episode, I did watch that show. But I had a very similar past in that when I was thinking about college and what I wanted to go to school for, I was thinking accounting, and to the degree that I actually started off college planning to get an accounting degree. And I lasted about two weeks before I went to the head of the math department, and we talked. We sat down for a while, and we ended up switching my plans over to grand mathematics instead.

ERIN: 09:03

Nice.

KENDA: 09:04

So it sounds like for both of us, it kind of started out with maybe an interest in these math classes and, like you said, something that was more accessible and more familiar to us, like accounting. And then, once we found out that actuarial science was a thing, we were like, "Oh yeah, that sounds cool."

ERIN: 09:20

Definitely.

KENDA: 09:21

Although I will say, if anyone has seen Zootopia, one of the first scenes, I think it is, the--

ERIN: 09:27

Oh my gosh, yes. I have to say, I have a pet peeve with that because they do not define it correctly.

KENDA: 09:30

Should we clarify?

ERIN: 09:33

We should clarify for everyone who's seen the movie that actuaries do nothing in regards to finding tax breaks. That would be a tax accountant. So for the record, Disney needed to do the research a little bit better on that one.

KENDA: 09:47

Yeah. So they are maybe getting a little more of screen time, but not in the way, maybe, that they should.

ERIN: 09:53

Yes. Oh, I think my one other appearance of an actuary that I saw as a child was also Disney, the Kim Possible TV show.

KENDA: 10:01

Oh yeah.

ERIN: 10:01

I'm pretty sure that Kim Possible's dad was an actuary.

KENDA: 10:04

That's actually really cool. I never knew that. All right. Well, maybe looking past formal education and schooling, looking back, what are some of the things you enjoyed growing up that might have hinted at your propensity for problem solving?

ERIN: 10:20

Oh, there were many. I was a funny child in that regard. So, for example, I have vivid memories of-- when I was young, my dad was the high school principal. And so I would-- and the middle school and the high school were attached to each other. They were separate wings of the same building. So I have memories of going down to his office after school, and I wanted to ride home with him rather than sit on the bus for an hour. So I would hang out in his office and just wait for him to finish up his day. And there was a room that was kind of reserved for meetings and it had a whiteboard in it. And at that time of day, there was never anyone in there. So I remember going in there and just making up these crazy, complicated math problems that I would solve on the board, like 10,384 divided by 7, because that was the age when we were learning long division. And I would practice my long division for an hour until he was ready to go home.

KENDA: 11:18

Typical kid things.

ERIN: 11:20

Oh yeah. The fact that that was how I chose to spend my time is probably indicative of my nerdiness. I thought math was fun. I really just loved the puzzle aspect of it. I loved puzzles in general. I did a lot of logic puzzles, jigsaw puzzles, sudoku. I would buy those books at the grocery store that would have all the puzzles in it, and I could entertain myself for hours just doing those. I was in total bliss if I had a logic puzzle book. So the fact that that was how I spent a lot of my time as a child was probably a good indicator I'd go into something involving problem solving. Another fun memory I have-- and this is just a plug for St. Jude Children's Hospital because I am very fond of their work. At my school, we did a fundraiser called the Math-A-Thon for St. Jude. And what they did was they mailed you this book of math problems. And you would get people to donate money. Like, for example, for each problem you get right, they'll give you $0.25 or whatever. And so you would get pledges from different people, and then you'd have to work through the whole book of math problems. And oh my gosh, this was so fun for me. I would just sit in my dad's office for hours and do all these math problems. And I just thought it was the greatest way to raise money for a children's hospital, was to ask me to do math for a while, so.

KENDA: 12:44

I mean, it's a win/win, right?

ERIN: 12:46

Yes, exactly.

KENDA: 12:49

That's [inaudible] so cool. I've never heard of that before.

ERIN: 12:52

Yeah, it's great. And I actually googled it to make sure that it still exists because my memories of it are from a long, long time ago, and they still do it. You can sign up your school to be part of the Math-A-Thon. So if there's any teachers listening, you should investigate this. It's a really great fundraiser.

KENDA: 13:10

And what age were you, about?

ERIN: 13:13

Oh, I would have been in probably like third or fourth grade. They have it for different grade levels. So they have a different book. It's meant to align with the math curriculum for a certain grade, so you can do it through multiple grade levels.

KENDA: 13:29

Oh yeah. Cool. I remember, when I was a kid, they had those books that you could do over the summer break. And these were just for fun that you could get from the store or something. And I always loved doing the math section of those.

ERIN: 13:42

Totally.

KENDA: 13:43

So I can imagine having some kind of drive like that to help you push a little further, too.

ERIN: 13:48

Absolutely.

KENDA: 13:50

But I agree. I also loved logic puzzles, sudoku puzzles. And I'm pretty sure I had a book that was just sudoku puzzles that I would do for the longest time. And then one Christmas, I got an electronic version of sudoku--

ERIN: 14:02

Oh, how fancy.

KENDA: 14:04

--and I would carry that with me to the grocery store, to holidays, family holidays. When my brother would be playing his Gameboy, I'd just have my little sudoku electronic game out, playing that.

ERIN: 14:16

That's awesome. I love it.

KENDA: 14:18

Yup. So that kind of covers more of, maybe, your younger childhood. And then as you grew up and became maybe more mature, more advanced math classes and started paying attention to the real world and the news more, what would you say was the first time that you remember your mind being blown by math or science?

ERIN: 14:39

Yes. So as I mentioned, I always enjoyed math. I thought it was fun. I thought it was a fun puzzle game, right? But the first time that math kind of took on a new level of interest for me was-- I'm trying to remember what grade it was that we first learned linear regression. It was probably maybe fifth or sixth grade; I'm not totally sure. But the idea of building a formula and identifying certain input variables that could predict a response variable, that was super exciting to me because it took math from being just a long division problem, which realistically, I was like, "I don't know when I'm ever going to have to actually do long division by hand in life. Even though it's fun, I don't know if I'm ever going to do that." The linear regression concept moved math into a whole new realm of, "Oh, people actually do this for a living. This is something that I could do."

ERIN: 15:40

And data is so vast. And, I mean, even just thinking about how much more data we have now than we did 20 years ago when I first started becoming interested in this idea of using data to predict things, I continue to be so excited by how much data we have access to and how much we can capture and what we can do with that information. I think it can really change the world in an amazing, profound way. For example, we have those little beacons from State Farm in our cars, and they collect all this data about our driving, like do we accelerate too fast? Do we brake too fast? Are we consistently driving over the speed limit? It captures all that information and sends it to State Farm, and then State Farm is able to use that in their insurance rates to give people discounts when they deserve it. Because if you're a good driver and if you're not driving aggressively, you shouldn't have to pay as much for your insurance as someone who drives more aggressively. So I think there's so much exciting stuff that can be done with this really basic linear regression concept, which started out as just literally drawing a line through [an?] X-Y axis points of data. But now, as an actuary, I see how that can become so much more complicated and be used in real-life settings. And I think that was what really kind of started my trajectory to becoming an actuary.

KENDA: 17:15

That's a really good point that you make, and it's funny that your example exactly relates to insurance and being an actuary, but I think you make a good point. Like I said, I think learning and retaining new information can have so much more meaning, and it's so much more exciting when you can not only first learn the theory, but then when you can apply it and see how it's really useful.

ERIN: 17:38

Yes, absolutely. And I think that's when it really started to become more of a career vision for me. I was like, "Okay, I could see myself solving these types of problems in real life and investigating which variables are most predictive." And trying to figure out, for example, what causes auto accidents? If you look at, over the course of a year, are there certain seasons when there are more accidents than others? And there's certainly correlations between weather events such as snowstorms and car accidents and stuff like that. So yeah, I think it's really fascinating, and it all kind of started there.

KENDA: 18:19

Yeah, that's very true. And I think when-- like you said, there's only more and more data every day. And when you're thinking about what career you want to go into and what you want to do with the rest of your life, what excites you, what blows your mind, thinking things like that that have a long trajectory are things that can continue to intrigue you is really important.

ERIN: 18:43

Absolutely.

KENDA: 18:43

And data and math definitely do that. So in light of this, kind of coming full circle, what is something maybe recently that you've been blown away by?

ERIN: 18:53

Yeah. Everyone's probably tired of talking about the pandemic, but I just have to say that I find the data surrounding COVID-19 to be fascinating. When the pandemic first started in March of last year, I was obsessively checking the metrics. More from a state of paranoia than fascination, to be totally honest. I was like, "What is this new disease? What's going to happen?" It was definitely driven out of fear rather than anything else. Now, at this point, I would say it's less paranoia and I'm over the initial shock that this is happening, and it's more just the interest in how this pandemic has evolved. I look at The New York Times almost every day. I'm a little bit obsessive about it. But what I love about The New York Times is they put together some awesome visualizations and maps. And I love the color coding they do to track the infection rates by county. And I just think there's so much that can be gleaned from the data that we have. And of course, I wish that we could get even more data around what causes outbreaks in certain areas at certain times. But it's been a very interesting year following the effects of this pandemic.

ERIN: 20:10

And to segue into a really awesome project that Kenda and I actually worked on together with one of our other co-workers, we attempted to build a predictive model for tracking these COVID outbreaks. And we used Alteryx, initially, to work on some of this. Alteryx has [a?] predictive modeling software powered by R within it. And so at our first pass at this model, we were using, I think Gamma and force models within Alteryx. So that's a pretty cool plug for a feature maybe you haven't tried in Alteryx. We used a lot of different demographic data to try to build our model, things like the age distribution within a county, like how many people in the county are over the age of 65 versus between the ages of 40 and 65, etc. We looked at how many people in a county are living in more of a confined space, such as a nursing home or a prison where the virus could spread more rapidly. We even attempted to add a variable around weather patterns because there's been a lot of speculation about in colder weather, the air is drier and the virus could spread more easily or remain airborne for longer. So we even attempted to throw that into the mix. We looked at a lot of different variables to try to predict these outbreaks. Kind of a sad conclusion to the story is that we determined it was really difficult and didn't feel comfortable launching anything, ultimately, because there were still so many X factors and so many variables that we couldn't capture, and we felt that it just wasn't to the level of accuracy that we needed. But regardless, it was a very fun thing to play around with for six months and definitely fulfilled my childhood desires of trying to predict the future, even though, in this particular instance, it's very difficult, if not impossible.

KENDA: 22:08

Yeah, absolutely. It kind of sheds light on two different ways of predicting the future. Like you said, your main day job is trying to create rates to charge insurers to cover losses in the future, and that's one way of predicting. But then, regression models is a whole different way of-- I mean, it could be incorporated, but a different way of looking at predicting the future as well.

ERIN: 22:33

Absolutely.

KENDA: 22:34

And in today's day and age, so much data can be captured and aggregated, and especially in the midst of this pandemic when everyone's just trying to stay safe, remember their mask when they go in the grocery store, or try not to run out of toilet paper. I think it's important, like you said, and interesting, even, just to take a step back and think about what all of this data and this big impact that this data has on our lives, as you said, from trying to predict the next outbreak or much farther than us to creating this vaccine that is safe for everyone.

ERIN: 23:10

Right.

KENDA: 23:10

So, unfortunately, we could go on and on, but I don't think Maddie will invite me back if we go over our time, so I'll stop us there. But thank you again for joining me today. I don't know about you, but it was fun to reminisce and kind of think about the human element and how it relates to data, and why it's so cool. I can't wait to see what other ways our minds will be blown by data in the next decade.

MADDIE: 23:38

[music] Thanks for listening. Join us at community.alteryx.com/podcast, where you can leave a comment directly on the episode's page to share the ways math and data have inspired your career path as an analyst, data scientist, actuary, or any other career that allows you to tap into your passion for data. Catch you next time. [music]

MADDIE: 24:08

Ta-da. For the record, I definitely would have let you keep going. [laughter]

KENDA: 24:12

Ah. When I said, "Maddie won't invite me back." That's okay. I had to use some kind of different conclusion that I didn't use yesterday. [laughter]

MADDIE: 24:21

No, it was so great. Honestly, I was so entertained the whole time that 30 minutes flew by for me. It was really, really great listening.

KENDA: 24:29

Sweet.

ERIN: 24:30

Awesome. Thanks, Maddie.

KENDA: 24:30

Yeah. Thank you, Erin. [laughter]

 


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

Comments
vbrown
8 - Asteroid

I LOVE Zootopia, but every time I watch that scene, I cringe a little bit.