Miriam Quick combines her journalistic integrity, authorial skill, and musical artistry to create narratives that truly resonate. Whether she’s writing about the hidden patterns in music, the statistical realities behind social issues, or the numerical trends shaping our environment, Quick sees the stories embedded in the numbers and brings them to life.
In a world overflowing with data, she believes it is crucial to interpret this information in ways that are meaningful and accessible. With her unique approach, Quick is helping to democratize data, making it more understandable and engaging for all.
“I’m always trying to find ways to use data that are creative and also constructive to communicate ideas that will hopefully kind of inspire people to look at the world in a different way.”
- Miriam Quick, Journalist and Author
This article features highlights from the Data Chats podcast episode with Miriam Quick on how creative methods such as artwork, sonification (creating music tracks based on data), and unconventional formats can make data more engaging.
Editor’s note: This conversation has been lightly edited and condensed for clarity.
How do you square creativity and data?
Well, I definitely don’t think of data as boring. I think a lot of that feeling comes from the formats in which we typically consume data. You might think of a spreadsheet and how it’s laid out with a load of numbers in black and white. And to many people–not to me, because I love spreadsheets–but to many people that might come across as dry.
I think it also has to do with the way that math is taught in school. You’re taught that either you’re kind of for math or against math. It’s almost like it’s this kind of marmite subject. And I think that can be really corrosive, particularly for women who might be put off by this stereotype of the math nerd being a male.
I really try to move beyond those sorts of stereotypes and show people that data is not dry. What’s really helped with this over the past ten years or so is the way that we now have normalized data. Because there’s so much data out there, perhaps we’re starting to lose this idea that data is dry. We’re typically consuming data through visualizations. Data can be exhausting in the form of spreadsheets, and visualizations are helping to break that down. These visualizations are extremely beautifully designed, and they involve a lot of creative thinking and are increasingly multi sensory. The balance comes from making sure the message is coming across in a way that is accessible for the audience.
Can you take us through how somebody might go from learning the basics of data visualization to doing what it is that you’re doing with data and visualizations? What are some of those steps along the way?
Well, I think I have followed quite an unusual trajectory. I came to work with data from doing a Ph.D. in music, specifically musicology. That’s why I’m so interested in sound and sonification and music, and I was always interested in communication and writing. So I did a lot of student journalism at university. Then I did my Ph.D. in music, and I got very interested in data through that. I was actually studying recordings and how performance style changes through recordings. And as part of that, I was making a lot of charts and graphics. I was working with tons of data on things like timing and intonation and how performers play and how you can encapsulate that using numbers. So I came to visualization from that background.
I guess I’ve always had this dual focus on the arts on the one hand and then data on the other. And when I first started, my data skills were really not very good.
Is there anything that you’ve learned with your unique background that you might be able to share with people who are taking a very different trajectory?
I think the main thing to learn is probably to play to your strengths. If you come from a computer science background and that’s what you’re into, then there will be something within that that makes you unique.
You might have an interest that you have that you can combine with computer science to create something really new. It might be a really strange, weird interest. I know somebody who is a really great developer, and he’s also a climber. He’s created some really interesting hybrid visualizations of rock faces and 3D mapping the rock faces. Now, that’s obviously quite a weird niche, but there will be something for everybody where they have that passion and that interest that they can combine with their knowledge to create something really unique.
When you’re experimenting with visualizations, how do you know which ones to focus on more, especially when it’s a large audience? How do you balance clarity and creativity? What questions are you asking and what kinds of decisions are you making in the process?
I think the approach can be somewhat different depending on who the client is. I will always have a brief that I will work on, and of course, everybody who’s working on these pieces is very clear about who the audience is. You’re not only writing the brief to clarify expectations, you’re also writing it for yourself. You want to point out what’s interesting.
For me, I like to do research in a lot of different areas, and I’m often approaching them as a newbie. Even if I might have a working knowledge of them, I’m still often approaching them with a beginner’s mind. It’s actually quite a helpful position to be in if you’re writing or making something for a broad audience, because you’re sitting in the same shoes as your audience, and you probably have some of the same knowledge and blind spots that they do. So you can approach it in a way where you ask yourself does it make sense to me? How do I understand this topic? What leaps out at me as most interesting? Where are the kinds of tension points?
How does your approach change when you’re working with a data set that somebody has given to you?
If I’m starting with a data set that somebody has given me first, I’ll have to spend a lot of time cleaning it, as I’m sure everybody does with data, and that takes up pages. Then I will do a lot of background research to try and understand what this topic field is about.
Where are the interesting questions to be asked? Where is current research? I do a lot of work that’s broadly scientific, particularly in the health and environment fields. So I’ll read news stories, and I will read papers on that. I will familiarize myself with the key terminology and make detailed notes about trends.
In terms of the data, it will be lots of exploratory plots and visualizations initially to reveal what the main trends are. I would use Google Sheets or Excel for this. Mostly. If it’s a bigger data set, I would use ggplot2 and R. Then, you’re looking for things that are either headlines that you can use to approach the story. For example, and I’m making this up, but something like only 13% of people are happy with their childcare. That’s an entry point into a topic.
Or maybe it’ll be a surprise–95% of British people are happy with their childcare. That’s of course also made up. But that’s an example that would surprise you because it’s not what you think it’s going to be.
Are there things about certain visuals that make them better for storytelling than others?
Yes, I think it comes down to the richness of the data that you have and the complexity of the data that you have. And I think that certainly when you’re thinking about whether to make something interactive, the question is, can a person fully understand this data in a static format or is it beneficial for them to be able to explore it? And although I really like interactive, they’re not as popular as they used to be, maybe five to eight years ago. But I really think that exploring data interactively is a great way to feel like you’ve got a stake in it and to be able to test things, to be able to tweak things. And it gives you kind of almost like skin in the game. And that is a much better way to get a story.
