Inside Outlier Conference 2022
Conversations with Data: #89
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Welcome back to our Conversations with Data newsletter!
In our latest episode, Mollie Pettit from Data Visualization Society talks to us about using human-centred design for her work.
Drawing on her circuitous career path from geology to data science, data visualisation and developer relations, she provides learning resources and useful advice for those new to the field.
We also hear about her favourite data projects and her inside take on this week's Outlier Conference happening 4-5 February.
Listen to the entire podcast on Spotify, SoundCloud, Apple Podcasts or Google Podcasts. Alternatively, read the edited Q&A with Mollie Pettit below.
What we asked
How did your career path begin in data visualisation begin?
While working as a freelancer remotely, I wasn't meeting other people in the field. This led me to start a data visualisation community group. I later co-founded the Data Visualization Society, along with Amy Cesal and Elijah Meeks. Next, I began at Netflix as a senior data visualisation engineer, where I worked on data-viz heavy web apps for internal decision-making purposes.
In 2021, I started a new role as a data visualisation developer at Observable, where I later transitioned to become the developer relations manager. The role allowed me to combine my development and community building skills. At the moment, I am currently organising the upcoming Outlier Conference as Data Visualization Society's events director and have done so since it began.
You call yourself a human-centred designer. Could you give us a brief overview of the concept?
Human-centred design is a great phrase. It's about designing with the humans, the viewers, the users at the centre of the thinking. It is rooted in empathy and about understanding the needs of who you're designing for. Human-centred design is about seeking to understand what people say and do, and how they think and feel. We can try to do our best to anticipate and make guesses about what people want. We all do this naturally by filling in the gaps and making assumptions. You might get some things right, but you will likely get plenty of things wrong.
This is where ideation, prototyping and iteration come in. So the first step involves ideating on the ways that you accomplish your goal. You can also flesh this out with user interviews and observations. Next, you choose a direction and build a low-fi version of something. You put it in front of someone and see how they use it and what they get from it. It is essential not to give them hints or clues. Instead, ask them to think aloud and say what they see or notice. It is vital to watch how they interact with it. This helps you to understand if you're going in the right direction -- is it confusing or not intuitive? But it can also tell you things that you've done right.
Who is the Data Visualization Society for? Are data journalists part of this community?
Data Visualization Society is a place where all data practitioners can intersect and connect. This includes data journalists, data designers, data visualisation engineers, data scientists, and anyone who uses visualisation in their work. The main driver for starting the Data Visualization Society is that we found that there were a lot of data visualisation communities, but they were very disparate and separated. We wanted to create one where people could connect across those fields. So, yes, it is a home for data journalists and many others.
Tell us about the upcoming Outlier Conference happening this week.
The goals of Outlier Conference have always been to create a space where attendees can make connections, inspire others and learn from others, all the while keeping accessibility and inclusion at the heart of these like planning decisions. This year the conference is happening on February 4th and 5th. We have an exciting lineup of curated speaker sessions and unconference sessions where attendees create the agenda themselves.
The conference aims to find ways for people to connect. In addition to attendees chatting in Slack, we also plan to have speed meeting networking sessions to help you make new connections and friends. The agenda goes back and forth between curated content with the speaker lineup and the unconference sessions. Last year we had talks, workshops, discussions, panels, games, or even virtual karaoke sessions. A range of ticket prices is available to accommodate everyone from the community.
Tell us about a favourite data project you worked on.
I'm most proud of the Illinois Traffic Stops project that I did with the ACLU. This is because it was part of an effort that led to actual policy changes. The purpose of the site was twofold. First of all, the site aimed to serve as a resource for the public to learn about law enforcement practices. Secondly, it provided a tool for law enforcement agencies to make informed improvements around racial disparities for the good of their officers and the people they serve. The site did not mean to be a finger-pointing device. Instead, its purpose was to help law enforcement think about making things better at their agency. Overall, the data showed there are some law enforcement agencies in which minority drivers are treated significantly differently showing some racial bias.
You learned D3 and also taught it. What advice and resources can you recommend for data journalists looking to do the same?
The first resource I used to learn the building blocks of D3 was Scott Murray's book Interactive Data Visualisation for the Web. I can't recommend it enough. It might not necessarily go into all of the fancy things you can do, but it sets the stage nicely for you. The next thing to do is pick out a project that you'd be excited to visualise with D3. You'll likely keep learning through that process. If you want to do something super specific, you can often find an example of someone who's already done that and shared it online. I'd also add that D3 is a very powerful tool, and some data journalists use it expertly. However, every data journalist doesn't necessarily need to learn.
What other tools can you recommend for data journalists who don't have the time or interest to learn D3?
Finally, who do you admire most in the data visualisation field?
There are so many impressive people in the data visualisation world. Gabrielle Merite creates really cool, unique projects. She brings the human back into the data. Duncan Geere and Miriam Quick have done some inspiring data sonification and data visualisation work. From a functional approach, I like Ian Johnson and Mike Freeman. I admire the elegant and functional ways that they tackle visualisation problems.
Alberto Cairo is always inspiring and I love reading his books. Jer Thorp is another person I follow and I'm very excited for his talk at Outlier this year. I find Jessica Hullman and Matthew Kay's work on researching uncertainty to be so valuable.
And finally, Nadieh Bremer and Shirley Wu are two designers who I admire for their creative ways of visualising data. I'm missing plenty of awesome people, but these are the names that first come to mind.
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Apply for Google News Initiative's latest fellowship
Applications are open for the Google News Initiative Student Fellowship 2022! It offers paid summer placements to journalism, technology, multimedia and design students as well as recent graduates across Europe who want to gain valuable work experience. Among the 30 participating newsrooms, 11 are looking for candidates to work on data-related projects. The application deadline is 15 March 2022, 23:59 CET. Find out more here.
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