Caro Buck | The Benefit of Talking to the \"Non-Datas\" | RStudio (2022)
Data literacy is a tool to build understanding- of the world and ourselves. Data, AI and tech are sometimes portrayed as scary and unknowable; however, data can be for everyone. Data, and decisions based off data, have enormous implications in our daily lives. We (data practitioners) likely have some baseline understanding of numbers and how to read a chart. But others, whether our friends, family members or coworkers, might not have the same level of understanding. This talk will address how to talk to these seemingly "non-data" people, the benefits of talking data with them, and (hopefully) encourage more curiosity and wonder at the creativity of data. We will also briefly cover what data literacy is and why we ought to care about it. Talk materials are available at https://rconf-2022-caro-buck.netlify.app/#/section Session: Working with people is hard
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Transcript#
This transcript was generated automatically and may contain errors.
Hello and welcome, everyone. I'm Caro, short for Caroline. I'm a data scientist at a marketing agency. I work on a creative tech team. Basically, that means I fall down weird rabbit holes and make cool data toys. That is not what I'm here to talk to you about today, though.
Here I am going to talk about why we should talk about people who think they are not data people. Back in 2020, we all kind of became armchair experts, sitting there pondering in our living rooms because we couldn't go anywhere else. If you were thinking of COVID, you and I are thinking of the exact same thing. All of a sudden, there was all this new jargon that came out, all these charts, all these numbers, all these stats, case counts. Flatten the curve was super popular, and if you didn't know what flatten the curve meant, you just pretended like you did so that you could keep up with everyone else.
But everyone was all of a sudden, like our lives were up front and personal with data. It would say whether or not we should go out, whether or not we should see people, depending on the case counts would determine should we wear a mask, should we see our friends, should we stay inside and do absolutely nothing else but bake sourdough bread. Really, data became very, very up close and personal. And data have always been there in the background, but it hasn't always been obvious. And so in this, I kind of had this realization that not everyone is interpreting data in the same way that I interpret data. And there was a lot of misconceptions that this brought up for me. So that's kind of what has inspired this.
I want to get us started with this quote, because maybe you're sitting out there thinking, I mean, I know something about data, but do I really have anything to share? Yes, you do. As we become more and more experts in our field, we kind of forget that not everyone is directly behind us following us down that rabbit hole. And so it's worthwhile to look around, remember our blind spots, and to be able to share with those around us.
What data really is
So those misconceptions I was talking about with COVID, a lot of people tend to think data are just charts or just a bunch of numbers or charts with stuff and more stuff. And it's just kind of this thing that maybe you just think about a lot. Or maybe it's really scary and data is taking over. Or maybe data is just for the nerds. Like, that's it. It's hard. We don't need to think about it.
And my response to these things is yes, and. Yes, data are all of those things on the previous slide. And so much more. In improv comedy, there's this theory of yes, and, that you take what your partner has given you, you take the setting, the characters, maybe the setup, and you add to it. You say yes, and you add something new. So I'm going to say yes to all of the data things on the previous slide, and then also add something new here.
So we're meeting people with where they're at and where they're coming from. So yes to all the things, and data is creative. It's not always black and white. There's a lot of gray. Doesn't always have the right answers, which maybe is a terrifying thing for you to think about. But it's also super scientific. It helps us interpret the world around us and understand information so that we can better understand the world that we live in and ourselves. And then as you look around, data are everywhere, which makes it really fun, at least in my opinion, to talk about data, because they're all over the place.
And then as you look around, data are everywhere, which makes it really fun, at least in my opinion, to talk about data, because they're all over the place.
So with that in mind, you're all formally invited to a data party. Yes, I realize we are already at a data party. We're at RStudio Conf. So here's yet another data party invite. But this one is just about talking about data. Wherever, whenever, because it's cool, and obviously with a lot of excitement. If you see, I'm very excited. It's not hard to get me excited about this stuff.
Who are the "non-datas"?
So let's hop into this invitation of what's really going on behind this data party. You might have noticed I throw quotes around non-datas a lot, like I'm a data person or I'm a non-data person. This is intentional. Usually I think this is a self-designated term, to be a data person or a non-data person. But really, everyone is a data person. We just exist somewhere on this spectrum.
So maybe you think you're closer to novice. Maybe you think you're closer to expert. Honestly, any of those is good and valid. It just matters that we all recognize we all exist somewhere on this spectrum and we can meet people with where they're at.
I also want to point out that the line from novice to expert is probably not straight. So don't think it's that easy. There's usually a lot of rabbit holes, a lot of twists and turns along the way. And there's also a lot of different end paths for expert. It doesn't mean that we will necessarily all end at the same place, which is why it's good to remember we all have blind spots, to look around, see what you know, and realize that people might be on different paths and it's worthwhile sharing things.
What to talk about
So when you're looking around for people to talk to about data, maybe it could be anyone. If you're working in an org, maybe it's someone who doesn't work on your team or it's a team that you work with more obliquely. So now that we know kind of who we should be looking for, who we should be talking to, what should we talk to them about? Data. But that's a very broad category. So I'm going to give you some more specific things to think about.
So since we're meeting people where they're at, we're saying yes and, we're taking their data knowledge and then adding something to it, what can we add to it? I like to think about this in terms of a grid, axis. That's how I organize my brain anyways. So we can move from less technical to more technical, personal to general. Let's think about this in terms of music, to give some practical examples. Because the what could be educational, it could be interesting, it could be a variety of things, but this is how we're going to break it down.
So if we keep it really not too technical and very personal, we can think of Spotify wrapped in that lower left corner. Spotify wrapped, you've probably heard of it, it's the end of year thing where it's like you listen to this many Spotify artists, you listen to this many playlists, this was your top five songs. It's pretty cool, it's pretty fun, but at the end of the day, it's just counting. It's like how many times did you listen to this song? So pretty simple data underlying it, although it is for your personal music, so it's fun to see how yours is different from your friends.
If we're looking for something still not too technical but more general, music borders is a really interesting article put out by the pudding, which if you haven't heard of the pudding, I highly recommend you check them out. It's a very interesting data journalism outlet that's very fun and interesting rabbit holes. This music borders looks at what is the most popular song in your city or your state or your country, and then you can compare all around the world. So what you think is super popular and that hit song, people may have never heard about on the other side of the world and they could be listening to something entirely different, which is super fun to think about as how music can connect us and be different but also a unifying thing.
And then if we're looking more technical, if we really want to go into the weeds with someone on some data, maybe we look at Nati Bremer's data art that pulls in a lot of different pieces of data into a visualization that really pulls together some interesting things, and we can talk about what goes into a good data visualization, what data pieces make something really interesting or really unique, and look at that. And then if we want to go super technical, pudding also has this really fun thing that will rant and rave about your Spotify or your Apple Music streaming. It is hilarious. But under the hood is an AI. So you could go deep in the weeds with someone explaining an AI, what goes into that, what kind of data are collected, and really go deeper with someone on that.
Where and when to have these conversations
So now that we know kind of some examples of what we might be thinking about as we're presenting, where and when should we be talking to these people? So if we're talking something more technical, it's probably worthwhile to maybe set up an invite with someone, put it on their calendar so they know it's coming, and so they can mentally prepare, get excited, maybe you send some documentation in advance, really set up for success for this conversation. Other times, maybe it's a more casual thing, and you're hanging out at a cocktail party, and you're able to just small talk away whatever latest data viz floated by in your Twitter that was really interesting that day. So thinking about where and when should we have these data conversations with folks really depends on the what. What are we talking about, who are we talking to, and at what level do we need to meet them?
Why data literacy matters
Why are we having this party? Let's remember why we're even going to go through this. So as we had these misconceptions at the beginning, or different conceptions of data, data are science plus art. It's the two together. Data are also an accessibility tool. I'll give you an example. Let's say I want to learn about penguins. I could go to the Antarctic and stare at penguins all day. I would probably learn a lot about penguins and see some interesting things. I would also be very cold and probably bored, if I'm being honest. Slash I have other things I want to do in my life.
So I will leave the counting of penguins up to the people who get super pumped about penguins. But in that, I can take their data and look at it in different ways. So maybe I will look at their data and read it in a book or in some text like this and see, ah, cool, longest flipper length of this penguin is this. Maybe I would summarize the data in a table and just have the summary stats like that. Maybe have a little bit less context, but I still see the data in some way. Or maybe I see data as a chart. And all three of these show the same kinds of data, but in three different ways, which is super important, because data themselves summarize information so that we can understand the world around us and we can present in different ways.
Data viz and accessibility and charts is a whole other conversation. I could probably make this chart better. I could probably make it much worse to be accessible. But it's interesting to think about how we can show data and what we can learn from each of these. Because you learn different things or you might realize different things by looking at the exact same data in a different way.
This is also super important, because we're not always talking about penguins, although we might want to. This also matters, because when we're in our daily lives and we have news feeds come up with different stats or different charts, we want to be able to understand those and we want others to be able to understand them and realize, what do I need to be understanding about this chart? What is the key piece of information I need out of this graph? And then also to realize, is this chart leading me astray? Could this be fake news? And being able to realize when a chart might be misleading is a very important skill to have. And so by introducing people to some simpler concepts or to even have these conversations allows people to realize and be able to understand data better in their daily lives as they go about them.
And being able to realize when a chart might be misleading is a very important skill to have. And so by introducing people to some simpler concepts or to even have these conversations allows people to realize and be able to understand data better in their daily lives as they go about them.
Resources and getting started
So maybe you are totally ready to get out there, to go for it, to start talking about data, and you are pumped. If that is the case, go forth. Do it. I fully support you.
But maybe you want some more resources. Maybe you need to sign up for a newsletter. There's some that will send out a chart weekly, break it down for you, and make it really interesting. You can listen to a podcast, read a book. This is one of my favorite books, You Look Like a Thing and I Love You by Janelle Shane. I'm getting some head nods. It's a hilarious book. It's all about AI. The title is a pickup line written by AI, so I feel like that should say enough for why you should go read this. Or just use that on your next thing. You Look Like a Thing and I Love You. Try it out and let me know how that goes. I would love to hear it. But really, this is just to prepare you to give you some thoughts of what to talk about when you're talking with people about data.
And my best advice is really just go out there and do it. It might feel super awkward and super weird at the beginning and people might give you some weird looks. Embrace it. Data is super exciting and we just need to jump in.
A story about data and creativity
So for this last thing, I want to close on a story. So back in 2020, the first week of working from home, I was super inspired by Georgia Loopy's data portraits. I thought this is super awesome. I want to do the exact same thing for my team. What a fun thing to have on our desks when we go back to the office, like a little name tag plate, a little fun welcome back thing. Spoiler, we never went back to the office. So I still made them though.
And it was super fun. I made them. We talked about them. It was super fun to have people guess who was who because I just showed them this screen and then I was like, guess which one you are. Some people got it. Some people didn't. It was fun. But it was an interesting way that we could talk about within our data team to learn how can we best support each other while we're all working remote? How do we best work remote? And having those conversations in a way that maybe you don't typically have. It wasn't like forced by HR, but it was this fun, creative, data driven way.
Additionally, our creative lead caught wind of this project and he's like, hey, Carol, I heard you did some cool data thing. And I was like, yes? And he's like, I'm going to have you present that to the entire creative team. And I was like, really? Okay. At the time, I was working on a team that did primarily like your classic marketing analytics, meaning how many people clicked on this email? How many people should we target on Twitter? How should we set up our tagging specification? So kind of like the bookends of a project. Like we'd help set it up and then we'd report on it after.
By presenting this project to our creative team that were mostly what they would self-designate themselves as non-data people, it blew their minds that data could be this creative, this interesting, this innovative. And it was really interesting for me also because then I got to have all these conversations after the fact with people about how can we incorporate data in new and innovative ways that we hadn't considered incorporating into our projects previously. And it was really interesting because we were able, when you're able to let data out of the box, to let your creative side and your data side work together, we can do so many cool things with data. We can do interesting projects and change the way that we perceive the world around us.
And it was really interesting because we were able, when you're able to let data out of the box, to let your creative side and your data side work together, we can do so many cool things with data.
So getting back to the point of why is it beneficial for us data people to talk to these non-data people? One, it's good to help them understand the world around them so they can interpret charts. That's all fine and good. It is also good for us because then selfishly we get to work on cooler stuff. If people are also pumped about data and we ourselves are enthusiastic about it, then we are able to bring these passions together, to share them, and to do really cool things.
So thank you. Thank you for coming to my data party. I encourage you to go start your own.
