[00:00:08] Lauren Burke: Welcome to Women in Analytics After Hours, the podcast where we hang out and learn with the WIA community. Each episode we sit down with women in the data and analytics space to talk about what they do, how they got there, where they found analytics along the way, and more. I'm your host, Lauren Burke, and I'd like to thank you for joining us.
Hello and welcome back to Women in Analytics After Hours. Today we have Jenn Schilling joining us. Jenn has over 10 years of experience working with data and loves sharing her knowledge with others. She's worked as a statistician, engineer, analyst, middle school math teacher and adjunct college professor. She also founded Schilling Data Studio where she helps others build skills in data viz and analysis. I am so excited to have her joining us and hope you enjoy listening along. So welcome Jenn, and thank you so much for taking the time to chat with me.
[00:01:09] Jenn Schilling: Thank you. I am so excited to be here. So thanks so much for having me.
[00:01:13] Lauren Burke: Absolutely. So like I mentioned just a minute ago, you have a really interesting background. You've held a lot of different roles and you've done a lot of different things that touch data and work with data in different ways. So can you give us a little more information about your background and the path that's led you there?
[00:01:30] Jenn Schilling: Yeah, so I studied math and um, computer science in college. And I got there because I really, really enjoyed calculus in high school. Which maybe that's a weird thing, but I just loved solving calculus problems. And so that's how I ended up studying math. And I found that I really loved applied math, so I love learning math when I could use it and apply it to solve problems.
And through that, I then found that I really, really liked data and exploring data and analyzing data. And when I was doing my undergraduate degree, I took a class in Operations Research, which is a specific branch of applied math about simulation and modeling and optimization. And my college had a Master's program in that field. So after I finished my Bachelor's, I stayed and did a Master's degree in Operations Research. And that's how I ended up in my first full-time job.
While I was doing my degrees, I did lots and lots of different internships and so I got to work in the defense department kind of area. I got to work in space science. I got to work in some food price indices. And then I got to work in supply chain, which is how I ended up getting my first full-time job as an Operations Research Engineer in the supply chain.
And I really love data and I love math because you can use them in so many different fields. And so just in that little experience of like my internships, I got to work in a number of different fields. So I worked in the supply chain for a little while, um, for a few years, and then I transitioned to a position as a statistician working in marketing and advertising research.
And then that's when I took a little bit of a break from the data and analytics world. Well, I wouldn't say I took a break necessarily from data, because data is used a lot in education, but I took a break from doing, um, statistical work, and I joined AmeriCorps and spent a few months doing college access.
And then I became a middle school math teacher where I got to teach at a STEM magnet school. And I actually taught basically like an applied math class for sixth graders for two years. And so it was a project-based math class, which was really, really fun. And then I spent one semester teaching eighth grade engineering.
And then I found my current position at the university where I got to work with education data, which is really important to me cuz I like working in the field of education. But I got to get back to that more focus on data and analytics and modeling, uh, and visualization. And I got into visualization I think during my first main like full-time job. I went to a Tableau training and I really thought that was pretty cool and I learned all these interesting visualization techniques and some best practices and I was like, "Huh, this seems, this seems fun. I like this." And I've always liked talking and sharing and communicating.
And so I think that's another that like helps tie together this like kind of data and analytics piece and how do we communicate it with people. One of the ways we communicate it is with visualization and the better that we can get at visualization, the better we can communicate our data. And so that's how I started to get into data viz and that kind of, continued throughout. I did a lot of my own kind of self-study by reading books, practicing, participating in things like Tidy Tuesday and Viz for Social Good.
And then I also incorporated it into some of my classroom teaching. And then even before I was in the classroom, I was actually going to schools and doing workshops. Or going to, you know, STEM days or career days, and things like that on the weekends and doing workshops. And I would do things like, let's visualize data. I'm like, let's make some graphs. And so, uh, I just, yeah, really love, really love graphs and visualization and teaching and education and that kind of all ties together now through my main job that I have. And then also through Schilling Data Studio, which I know we're gonna talk about a little bit later. But that's my consultancy and training business that I have on the side.
[00:05:19] Lauren Burke: That's awesome. I love working with younger kids, especially when you're trying to find fun ways to teach them about data visualization and just data analysis in general. I, before I did this one where they had little mini basketballs and they would shoot it from different distances and try and visualize how many they were able to make from different distances. And it's just a really fun way to capture trends and um, get them to make a live graph. So I, I think it's really cool that you've done that because it's so fun watching young minds be shaped and have them figure out that, wow, data and math and computer science are really fun. We just have to apply them in ways that capture their own interests.
[00:06:00] Jenn Schilling: Yeah. Yeah, though that, that's awesome. I love that. I love that idea. Like the basketball trends and visualizations. And yeah, exactly. I feel like math gets a bad rap and people just like, oh, I hate math, or I'm not good at math. And math is in pretty much all parts of our lives and whether we like it or not.
And so that was one of my motivations for wanting to teach was that there's all these cool things we can do with math, and I wanted students to be able to see those cool things. Especially in middle school, which is such a tough time. Um, just generally a very tough time. But it's also a time when students are starting to make decisions about what they're good at and not good at. And those decisions can shape where they end up in their future careers. At least initially. I mean, we can always change careers, but that mindset that we get of what we're good at and not good at when we're developing our, you know, in our adolescence. Like that plays a really big role later on or it can. And so that was one of the reasons I wanted to go to go to middle school was just because I was like, math is, math is cool. Like, I know that might not be a popular opinion, but I think math is cool and I wanna help you see how math is cool.
So we did do projects, like we did a party planning project where they, um, had a budget and they had to figure out what they were gonna buy and ratios of different categories and graphs. Then they had to graph like how they distributed their money and things like that. And we did another really fun project with 3D printing where they designed, the premise was that humans had gone to another planet and they had all these different problems. And so different groups of students were given different problems that the people on the other planet had been having and they had to design some sort of solution.
And we ended up 3D printing their designs. And yeah, it was, it was really cool. It was also really, really, really hard. Teaching middle school is a very, very difficult thing and I have the utmost respect for all teachers. Um, and I loved it and it was hard and I just wasn't cut out to do it super long term.
[00:07:58] Lauren Burke: Yeah, it is a really interesting age, and I think there's been studies that, I think fifth and sixth grade is when girls decide, or a lot of them fall off the track of, "I like math, I like computer science, I have the potential to be good at that and do interesting things with it. So I think that's just such a good age to be allowing them to explore the fun ways that can apply things like that. And I really love the 3D printing aspect. That's such a cool full circle project where I feel like they would be interested in every step of it. Instead of kind of getting caught up in like, oh, now the data part. Oh, now the math part.
This is less fun. And really keeping that enthusiasm. So that's really cool.
[00:08:36] Jenn Schilling: Yeah, that was a really cool opportunity I had to partner with, um, actually the university I work for now. Because the middle school that I taught at was right across the street from the university, so we had really close ties between them. So that was a really neat opportunity for me and for the students.
[00:08:49] Lauren Burke: That's so cool. And so speaking of your current role at the university, you are a Senior Research Analyst, and so with data visualization, what do you think the role and impact of visualization is in higher education?
[00:09:04] Jenn Schilling: Yeah, so visualization is so key to communicating data, and I think especially at a university. So at a university, we've got data on our current enrollment. You know, students who are at the institution, students who have been admitted, are they going to enroll or not?
And then we also have data on the faculty and the employees. So we have like HR based data as well. And then we have data on research proposals and research grants and how much funding we're getting, what types of grants are getting funding and where is that funding coming from. Among other things. That's just like some of the data that exists on like about a university.
Not to mention all of the data that is going on with everyone's research, but my focus is on, you know, the data about the university, about the institution. And I, I, and I even forgot to mention like budget data as well. That's a whole nother thing about like finances and the incoming revenues and all of that.
So there's a huge amount of data, huge volume of data, all kinds of different types, and that gets shared in a variety of ways. So some of it's shared publicly. Some of it's shared with students. Sometimes we share things internally with faculty and staff, and then also with university leaders. There's also pieces of it that get submitted to the federal government and the state government as well for different, you know, funding requirements from the institution.
So there's a lot of data and a lot of data sharing. And one of the ways to share that and communicate it with, especially faculty, staff, public, students and and our university leaders is to share it through data visualization because charts make data more approachable, they make it more understandable. And access to data can help people make better decisions and better understand the trends at the institution. And so by using visualization, we can help improve that access to the data so people can look at the trends, make better decisions and things like that.
And so just as a few of the examples of like things that we've done with visualization. When we had COVID 19, which we, I guess, is still going on, but we're not quite in the thick of the pandemic anymore.
Um, but that was a huge place where data visualization became key. Because we needed to communicate metrics at the institution of what does it look like. Okay. We're testing students like how many tests are coming back positive? We're also testing faculty and staff, like how many of those tests are coming back positive?
We were publishing those numbers publicly as well. So we had a COVID dashboard that I got to develop in partnership with some of the different university initiatives folks, to how do we communicate this data out publicly? And one of the ways we do that is through visualization. And then we had all of these internal things as well that we were reporting on like wastewater metrics and stuff like that, so that we could share that with the university leaders and they could make better decisions around should we test a whole dorm? Do we need to? Are we filling up on isolation rooms? Like those kinds of things. And then we have other public facing dashboards just around like our demographics data. What does diversity look like at the institution in terms of the student population, in terms of the faculty and staff population.
And then high level reports, too. Those involve a lot of visualizations to look at our current enrollment trends or current staffing trends and things like that, as well as evaluating different decisions. So if we're going to think about changing how we distribute scholarship money, how do we make that decision?
How do we figure out what variables are important or what measures on the student are important to determine their success at the institution? And we can do a lot of statistical modeling around that. But then in order to share that with someone and have them actually get some insights and be able to make a decision, we have to use something like visualization in order to communicate that.
[00:12:40] Lauren Burke: That's awesome. That's so interesting. I honestly, when I asked you this, I was expecting most of it to revolve around mainly education specific data, right? Students and faculty. But you're also delving into a lot of the community around you and the resource management, which is really, really interesting.
Just how many different types of data you are dealing with and being able to communicate. Because no matter what your opinion is, when you're faced with the data, when it smacks you in the face, it's hard to maintain that opinion if it's wrong. Right? So that's really cool that you're able to affect so many different types of decisions based on just the different data that you're communicating on.
[00:13:18] Jenn Schilling: Yeah. Yeah. Without the data to back up a decision, then you don't always know if it's the right decision or not. And as you said, data can definitely like kind of sway someone's instinct on something as well. So yeah, it's crucial to have data to make decisions at a university level, and the visualization is a huge way that we can communicate that and share that with people.
[00:13:39] Lauren Burke: That's so neat. So speaking of data communication, data visualization, and you are obviously very skilled in it. You have taught a lot of people to improve their visualization skills, and in the last year you've founded Schilling Data Studio, which is your own consultancy and training firm. So what inspired you to open the practice?
[00:14:00] Jenn Schilling: Yeah, so I just love to learn and share what I know. That's a big part of it, is I just love learning. That's one of my, whenever I do strengths finder like learning comes up as one of my strengths. So I just, I love to learn and I also love teaching and I love getting to share what I know, so that that's a big part of it.
Another big part of it is that I want more people from diverse backgrounds working in the field of data, and so I want to do my part to share what I know, and help people improve their abilities to analyze and communicate data, because data's just becoming more and more ubiquitous and is being collected on more and more of us, and so we need more people who look like the general population. Like we need more representation in the field of data. And so that was another part of it too, is I wanted to share what I know, create some trainings and tools to help people who want to come into this field.
Here's what I know about how to communicate data well, and that's another big part about this graphs and like the data viz part, right? Specifically about the knowledge that I know, like I know how graphs and charts can be made more effective and engaging, and I want more people to have that knowledge because as we get more and more data in our lives, as we see much more and more data in the news in our apps, like in reports all over the place, I know how we can present it more effectively. And also I know how to help people save time in creating graphs and charts as well. Cause once you know these design principles that help you make more effective and engaging graphs and charts, then that saves you time as you go forward. And so I want more people to know about that. I want more people to be creating awesome charts.
I also just love teaching and speaking and so through Schilling Data Studio, I get to do that. And so it's been a lot of fun so far to kind of build this and grow it and see what it'll become. I think a big part of it that I want is to build resources for people and also to hopefully build a community for people to learn in as well. Because I think that's really important, especially if you enter a field and you're not sure where to start or you just don't have a lot of people who you can identify with. I think that if I can build a community where people know how to start, they get to learn these skills and they get to interact with people who they can connect with, I think that could be a really powerful thing. So what inspired me to open it. Just loving to learn, loving to share, and wanting more people to have the knowledge around how to create great charts and graphs and how to do it in an easy and efficient manner.
[00:16:22] Lauren Burke: That's really cool and I was able to take a look at your website and you have a ton of resources and courses that you've developed and that you have available. So I highly encourage anyone interested in building their skills or learning a little bit more about data vis and how you can better design your own to go check that out.
[00:16:40] Jenn Schilling: Thank you so much. I'm so glad that you were able to check it out and that you found it helpful.
[00:16:44] Lauren Burke: Absolutely. And so one of the things you've developed resources and courses around is the data viz design process. So for our listeners, why is it so useful to have a process in mind when you're designing a viz?
[00:16:57] Jenn Schilling: Yeah, so a design process provides a step-by-step framework for completing a project. And in this case, a project is basically creating a graph. And when you have a framework, it helps streamline your work because you follow the same steps each time, and that makes it easier to complete a task or complete a project.
And when you don't have to think about every single step, like, what do I do next? Then you can think more about your design and what the message is in your data and how to best communicate it. Because you don't have to figure out, oh, what's the next step? Oh wait, I did, I forget this thing. Oh, I have to go back and do that thing. Because you just, you just follow this process.
So I used the premise of the design thinking process to build my design, my database design process, and. Focuses attention on the audience and on the user. And by doing that at multiple steps along the way through the process, then your final product, in this case, your final visualization or maybe multiple visualizations are going to be better because the audience is being considered. And so you're thinking about who's this for? How can I best communicate to them? And all of those kinds of things as you're building it, which results in a better end product.
Instead of the way that I used to design graphs and charts, which was, well, I think this is interesting and let me figure out some ways that I like to visualize it and I like to plot it out and things like that. And at the very end, oh, who's gonna look at this? And did I use colors that are gonna work and that are accessible? Did I highlight the right information? Oh, I chose this really obscure chart type that I think is really cool, but I'm gonna show this to a bunch of people who aren't used to looking at data and maybe I, I need to change something. So when you're, when you have this process that focuses your attention on who's this for at the beginning and gives you this step-by-step framework, it just makes it a lot easier all the way through.
[00:18:46] Lauren Burke: That's great. And one of the things that you've talked about before in that process is thinking about bringing in more accessibility and empathy. So what are some ways that you do that with your data viz and data communication?
[00:19:00] Jenn Schilling: Yeah. This is such a great question because it's so important to bring in empathy to data viz and data communication, because not only do the choices we make in designing a data viz affect the end user and the decisions that they might make, but it also affects the people represented in the data. And so we can bring this idea of empathy into our data viz and data communication by making sure that we're reflecting first on the individuals represented in the data.
Now, not all data is about people, but a lot of it comes back down to people. Even sales data, like somebody purchased those items. So a lot of our data at some level is about people. Working at the university, all my data's, almost all of it's about people. But depending on your field, there might be varying degrees of separation, but usually there's some people involved somewhere in the process. And so we need to make sure that we're communicating truthfully and adding context to explain and add depth to that data and add that personal information of like there's people behind these numbers. And if we're not reminding our audience of this, at least we need to keep in mind as a visualizer and an analyzer that what we're presenting could impact those people.
And if it's possible, I think a really awesome thing to do, um, which, you know, we don't get the opportunity to do very much, at least not in my field, or not in my experience I should say. Is speaking directly with the people represented. If you get the opportunity to do that, then you can learn a lot more about their background and their stories and you can use that to add context to the data.
And so it's hard to remember as I said, that these data points would represent actual people. Cuz when we're just looking at numbers on a screen it can feel very abstract, but one way to bring in this empathy is to remind ourselves that there are people behind these numbers.
And then another way is to think about the audience and some of the things I was just talking about with that design process. So who are we communicating to? What do they need to know? What's their experience with this particular data? And with data in general? How do they feel about data? You know, are they comfortable with data or is that something like super uncomfortable that they're not really sure about?
And so that's another way we can empathize. Thinking about the audience and what do they need and want, and that can help guide our analysis, the key points we address with the data. We can use that to make chart choices and choices about how we present the information as well. And it can help if we need to provide some additional supports to the audience to help them understand the data.
We also can think about the assumptions they might be bringing in, like we mentioned earlier, like people who might be making decisions based off of their instincts, and then we bring in some data. If it goes against their instincts, we're gonna have to think about that before we present it, because we're going to have to provide some explanations and context around where this data's coming from, why they can trust it, like that kind of thing.
So in the end, I think the goal of visualization is to support understanding. And through that, sometimes decision making through that, you know, trends in data and things like that, but generally like supporting, understanding. And so I think empathy's a really key part of that goal of trying to help people understand through data.
[00:22:02] Lauren Burke: That's awesome. There are, it sounds like, a lot of things that you need to keep in mind when you're creating a viz, other than thinking about well I have this really interesting data set or this really cool idea for a visualization, but actually making that accessible and interesting and applicable to the audience you're trying to share it with is a lot more complex than it looks at face value.
[00:22:23] Jenn Schilling: Yeah, definitely. And yeah that accessibility piece too is key as well with the empathizing too. Like thinking about the needs of your audience and how are you going to meet those needs. Whether it's making sure that your color palette is accessible for different types of color blindness, whether it's making sure that you have a description of your visualization.
And so if you're presenting it on the web, you can put information into the alt text that will be helpful to people who use screen readers. And so that part as well, I think is another piece of that empathy. Just thinking about others, right? Thinking about how you make your visualizations as approachable as possible to your audience.
[00:23:02] Lauren Burke: That's awesome. And that's how you communicate it to a larger audience and make sure that the population able to view and make decisions or have an impact based on the information you're sharing is able to be communicated to as many people as possible, so that's awesome. But over your career, what's been your favorite visualization to create?
[00:23:21] Jenn Schilling: Yeah. This is a tricky question cause I was like, I am not sure. Um, I really enjoy getting to work on the COVID 19 data at the university during the pandemic. And actually our Covid 19 dashboard is still going and it's still publicly available and we still have a report that gets sent out nightly.
But getting to work on such a critical project like that and at a when we didn't have a lot of information, it was really, really important that we got information out to the community. And I mean, there were people who looked at it every day, you know, from the general public to our faculty and staff and to students to help inform their decisions around whether they were gonna stay remote or things like that.
And so that was a really cool opportunity to get to get to work with that project, work with the people on that. There are so many amazing people working on that project because, we had to work very quickly and pull in a lot of different data sets and things like that. And so my responsibility on it was building out the dashboards and figuring out how we were gonna present this information in a consumable way to the public and to our university community, and then also internally to our stakeholders. And so that was a really cool project to get to work on in a professional way.
And then one of my personal favorites, like for a personal project. I, I love to read and so I started in 2021, tracking all of the books I read in a Google spreadsheet. And I know that there are apps and things like that that you can use, but I just use a Google Sheet. Um, my own tracking method and, uh, at the end of 2021, I visualized all of my books and I made these like book stacks and it was really fun and I really, really liked it. And so I did the same thing in 2022 and I just made my new versions of it and now I'm making some comparisons between 2021 and 2022.
And so that's just been a fun personal data viz that I really enjoyed. This one about like books I read because it ties in my huge enjoyment for reading with my enjoyment of data viz and I make it an R, which I just love programming an R and creating visualizations in R. So it's just a lot of things that I enjoy and I just get to play with some data that's just for me.
And I mean, I do publish my visualizations, but you know, it's just my data and it's like a nice reminder too, that yeah, there are lots of publicly available data sets out there that are fun to explore, but you can also just pick something that interests you to practice on, to analyze or visualize or build models off of. Whatever part of data analytics you want to do. You can it just pick something you like to do or you're interested in and then play around with that.
And one last one, because I obviously can't pick one. Um, but a third one is that I got to through some work I did when I was working with AmeriCorps, working in college access. I then got connected with the Arizona Commission for Post-Secondary Education and I built some dashboards for them that tracked FAFSA completions in the state and tracked data by high school.
And so FAFSA, which is the Federal Application for Student Aid, students have to fill that out in order to qualify for certain federal grants for college funding. And a key indicator of college going and college readiness is completing that document. And what we did is, we took the publicly, well, the FAFSA publishes every week, the completion rates by every high school.
And so we took that data and, uh, I processed it all and put it into dashboard format. And so we could look at by county, how are things looking? Let's look at school district, let's look at specific schools. Once we'd been doing it for a couple of years, we could then compare to the previous year, and that was just a really cool project to get to work on as a kind of side project.
And again, it ties into my strong value of education. So that was kind of another personal favorite because it was fun to work on and really interesting. And also it got to be one of the ways that I feel like I can help support education in the state.
[00:27:10] Lauren Burke: Those all sound absolutely so interesting. The COVID one especially. That's such a unique situation because the data you're getting and you're working with is ever changing, and the situation keeps being updated. The decisions you're trying to make keep being updated. All the while you're rushing through to create a visualization that you know will be viewed by a ton of people.
So that design process really matters, but you're also on a deadline. So that's a really interesting, uh, project where you had to balance a lot of those different aspects with also that time deadline that probably made some of those decisions have to move a little bit faster.
And then I love the visualization where you're tracking the books you're reading. That's such a good point that you don't have to go find a data set, you could make your own data set. You can even be the data set basically. Based on things you're doing or things you enjoy. So that's just such a cool thing that I love that you were able to do.
[00:28:00] Jenn Schilling: Yeah. Yeah. No, I really enjoy that one. And yeah, with the COVID one, there was a lot of rapid prototyping. Like, all right, let's pull in what we've got so far, and just like, does this work? Does this work? Okay, we're gonna put this out there and we'll revise it again when we get some more data.
And so there have been so many versions of these dashboards because this, right? We're trying to get the information out as quickly as possible, and in a understandable format, right? Like not just wanna put out like just, you know, poorly designed stuff, but wanna get it out in a well designed format. But what's our minimum like viable dashboard that we can put together that is accurate, well designed. Okay, let's do that and then we'll come back and see what, what, okay, now we've got this new feed coming in. Now we know some more about the occupancy in the isolation dorms. Okay. How are we gonna integrate that in and how are we gonna explain to people what these different indicators mean and things like that.
And then we've had, because it's been going for so long now, some of our data feeds are no longer being updated. So then we've had to kind of like remove some stuff. Um, so yeah, it's was very interesting and, and because it was, especially the beginning, like being viewed so much, we actually got a lot of input from people who are looking at it, which is really nice.
That's something you don't always get, is input from your users unless you go out and like do a focus group or. You know, you can just hear from the people who are mad. Um, so we got, we got both, we got positive feedback and we got questions too, which was really helpful because that helped us identify, oh, that's not clear. We need to add some notes and help explain how we actually are figuring out this metric or like, why this is presented in this way. So that was a really, really neat process to be a part of.
[00:29:43] Lauren Burke: That's awesome. And that is so cool that you were able to get so much feedback because you said it exactly right. You usually don't get feedback unless people are mad. So, that's so cool. Um, but a little more on the fun side. So data viz is a very creativity driven area, but we often focus on visualization with tools like Tableau, Power BI, and Looker.
So what are some other methods that you've experimented with that visualize data in a more non-traditional way?
[00:30:11] Jenn Schilling: Yeah, so I've always been very inspired by the Dear Data Project from Giorgia Lupi and Stefanie Posavec. And I generally also just find Giorgia Lupi's work very inspiring from a design and creativity standpoint. She's worked on some amazing projects. Even to like, the one that always comes to mind to me is where she, I don't remember the fashion company that she partnered with, but she partnered with a fashion company and designed three different sets of out, I guess like outfits, like clothing and bags.
I think there were as well, like for these three different sets, and each one was based off of a woman in STEM. It was just like, what? That's the coolest thing. Like, I mean, I, I am very grateful for all the cool stuff I get to do. I do get to do a lot, a lot of cool stuff and then I'm just like, wow, that's also so cool.
Um, so I just am so inspired by her work and I really love her take on data humanism. And I've experimented a little bit with data visualization through drawing. Um, I have one that I created a few years ago on tea that I drank for a week, and I like kept track of like the time of day. How long it took me to drink it, like how long did it sit on my desk until I consumed it and like what type of tea it was.
And then I made a more recent version, or a more recent collage that actually also was based off of beverages I drink. And I didn't realize, so I was like thinking about, oh, when have I done these things that I focused on beverages that I drink. Um, but I really love tea and I also really try and stay hydrated.
So maybe that's why I'm just like, all right, well if it's not about books, it's gonna be about tea.
[00:31:49] Lauren Burke: And they pair well together.
[00:31:51] Jenn Schilling: Exactly, exactly. So, with that collage, that was for a Nightingale data challenge, that was to make a visualization without, without drawing, essentially. Like, without programming, without drawing, like using different objects.
And so with that one, I tracked like, okay, I drank like five glasses of water throughout the day at these different times. And in between I drank this or tea or whatever. And then I cut out pieces from a magazine and used certain shades for certain types of beverages. And shapes as well, and then put them together. And that was really, really fun. But I actually usually end up making most of my personal visualizations with R. And then with work, I use Tableau and then an Oracle BI tool that we use, and then sometimes some with R as well. So I do a little bit here and there on my own with this kind of like drawing and collage and things like that.
But even, even most of my own personal stuff I end up doing in R. And I think part of that comes from just, I have this strong training in math, right? And so sometimes it's easier for me to put the data into the computer and play around with it there than it is for me to try and do it on pen, like with pen and paper or something like that.
But I hugely inspired by people who create visualizations through all kinds of different methods like textiles and sewing and knitting and drawing and painting. And there's just endless possibilities, which I think is really.
[00:33:13] Lauren Burke: That's so awesome. I love just all of the different ways we have available to communicate insights. Whether it's a physical medium like you were talking about, or a software based one where we're able to look at it in a real-time way, a click on things, zoom in, zoom out.
And a lot of newer technologies are allowing us to look at things in a more intricate or less aggregated way. So things like 3D modeling, virtual reality, they're bringing a lot of new possibilities to things like medicine, engineering where you can really zoom in and see things at a molecular or really in depth level. So what other kind of trends and advancements are you excited about in the data viz space?
[00:33:53] Jenn Schilling: Yeah. A couple of weeks ago I actually saw a post in the Data Viz Society Slack channel about a data visualization sculpture that shows the people mentioned in Hungary's most famous poet's letters, poems, and notes. And so it's like the circular, sculptural kind of physical piece that you can go and see, but there's also an augmented reality layer that allows for further exploration of the data and the patterns. And if anybody is listening to this in Budapest, I guess you can go see it at a museum there. Um, I've only seen pictures.
But I think that there's a lot of opportunities there with like virtual reality, augmented reality, things like that to enhance data visualization and create a depth to a data viz where the user can explore their own paths through the data.
Because data viz is often just a summary of the information. So the opportunity of these technologies for depth and exploration is pretty exciting. And then as you mentioned, there's also, as we advance into our imaging technology and things like that, we can get further into detail on medical imaging and other things like that, and satellite imagery, and that's just all really amazing.
I think there's a lot of opportunities there. And then generally, I guess, about the data of viz space and kind of other trends and advancements. I'm really excited that there's more resources and programs coming out around data literacy and curriculum around data literacy being developed for K-12 especially. Because that's so critical as we have this kind of more and more data infrastructure in our lives, like being able to understand when data's being collected about you, being able to understand data when it's presented to you. Being able to analyze some data like at a basic level. I think those are just like fundamental skills at this point.
So I'm really glad to see more and more data literacy programs coming out. I'm also excited that they are more no code or low code options for creating data viz now. Have already mentioned I really like R so I just like to code. But I know that not everybody loves to code. And so I'm really glad that there's more and more tools coming out like that, that are more like put in your data, and now here are some charts and you can do some customization and you can play around with different chart types. But it's not, it's not as complicated as here's a blank text file, right? So figure out how to get your data into the program, figure out how to write the code to make your visualizations.
As I said, I personally love that and I love teaching people how to do that, but I know that it's not for everyone. So I'm glad that there are more options that are making creating visualization more accessible to people.
And then a final thing that I just was thinking about this morning, because I'm reading The Functional Aesthetics of Data Visualization by Vidya Setlur and Bridget Cogley. One thing that they've been talking about in that book some is natural language processing with visualization. And I honestly, before reading this book, didn't even realize that was a thing. And so some of the visualization tools we have now, a user can type in a question and it produces, the visualization either filters down to the right data or it will annotate a certain point.
It will, you know, the charts will change based off of the user's question and I think that's really cool. And so I'm excited about this increased user interactivity with the data and with data visualizations, because at least for the stuff that I produce, you know, we try and add some interactivity, right? There's filters. You can zoom into certain areas. Sometimes we give you the option to change the chart, right? You can look at this as a table or you can look at it as a bar graph. And then some of our dashboards, you can go in and analyze the data further yourself.
But this idea that you could kind of ask a question and it would be interpreted by the program to then produce output that answers your question, I think is really cool.
So I think there's a lot of opportunity there and I'm just kind of been thinking about that. Cause I as I said, I've just been reading this book, um, over the last few weeks and as I was thinking about things this morning, I was like, oh yeah, that is really cool.
[00:37:43] Lauren Burke: That's so interesting. Yeah. I feel like that will be an absolute game changer because especially if you have stakeholders that are interested in now they wanna see how this question applies or how do we answer this, maybe you can type that in and that question's already in there. You're just not filtering it or you don't know how to filter it, but with that ability to add natural language processing, right? It can filter it for you. And that's another step that is already built in, in your self-serve analytics, self-serve data visualization process. So that's awesome.
[00:38:11] Jenn Schilling: I think there's a lot of opportunity there. And it helps with some of that as you're saying, like if you don't know exactly how to interact with it. Well, if there's a question box, like most people now are pretty comfortable with like searching the internet for something and so it then just becomes another search. It'll present you kind of the visual of the information that you're looking for, instead of you having to know, oh, I clicked down on this thing and have to select this box and unselect this thing over here and select these dates, and yeah. So I think it could help a lot with, you know, more people getting access to data.
[00:38:42] Lauren Burke: That's awesome. And so you've already given us a lot of resources to think about over the course of our chat, but I'll ask you for one more and that is, what's one resource that's helped you in your career and that you think might help others listening?
[00:38:56] Jenn Schilling: It is hard to pick just one. Um, but I think broadly speaking, like mentoring has helped me a lot. And so one of the things that I like to try and do is participate in mentor programs now as a mentor, because being a mentee was really helpful to me in my career. And I've mentioned the Data Visualization Society. They have a mentoring program. And then I think that as well just generally creating community around people doing data visualization. I by participating in that community, actually, that's how I became an adjunct faculty member. Is I saw a post on the Data Visualization Society and I was like, oh, hey, I'm interested in that. And I have gotten to write some articles for Nightingale, the Data Visualization Society's publication. And so I think there's just a lot of opportunities within that one organization.
And then if I can give a second thing, the other piece that has helped me a lot is just practicing. And whether, as I mentioned earlier, that's on my own data. Just practicing in the tool that I want to learn, practicing some of the principles and design that I've read about and seeing how I can make my visualizations more engaging, more effective, more understandable. And one of the tools that I've used to practice a lot is Tidy Tuesday, which is a weekly data set that's put out in the R community for people to create visualizations and practice analyzing and processing data on. So that's been another really great resource for me.
[00:40:22] Lauren Burke: That's awesome. I think those are all amazing resources and we are definitely going to link those in our notes section, so everyone should absolutely check those out. Um, but before we wrap up, how can our listeners keep up with you?
[00:40:34] Jenn Schilling: Yeah, so a great place to keep up with me is on Instagram. I am @SchillingData and Schilling is S C H I L L I N G. And then also on my website, which is schillingdatastudio.com. And if you go to schillingdatastudio.com/resources, that's where you can actually access the data viz design process that we were talking about earlier, which is actually a free resource that I have.
And it has a video training and you can also see the other online courses and resources and programs that I have available through that. So those are the two best ways. I'm also on LinkedIn and on Twitter, at datavizjenn. So there's lots of different ways to reach out for me. But Instagram and my website are the best ones.
[00:41:13] Lauren Burke: Awesome. Thank you so much Jenn. And absolutely go check out her website, go to the resources tab. I did. I downloaded a bunch of stuff. It's all really awesome. But I really enjoyed having you on today, Jenn, to talk to us about your journey, about data visualization and how we can improve the way we're building our own, and just having a fun chat.
So thank you again for joining me.
[00:41:34] Jenn Schilling: Oh, thank you so much for having me. I had the best time and I was so excited to get to be on the podcast, so thank you.