Stakeholder whispering by Bill Shander is an approachable book about why it’s important to solve the right problem, and how you can make sure that you’re doing it. Having worked with many designers over the years, I can say that stakeholder whispering is the hardest part of the job to get right, and often the most important one. The book offers simple, clear advice on how to make sure you’re getting to the bottom of a situation before diving in with solutions. 

It can be very hard to whisper well. Consequences for failure can be severe, but there aren’t a lot of books that focus on just this one aspect of working with a team. This book offers guidance from an expert whisperer on the small things that might trip a new designer up. Reading it is like shadowing a senior designer at work.

Bill brings the reader along at a level that’s gentle enough for a beginner but also valuable for an expert. Written with empathy and a sense of humor, the book feels like a comfortable conversation over tea with a friend, commiserating and sharing tips with someone who has had all of the same struggles and knows what it’s like. At different times, I found myself laughing out loud, grimacing in recognition, and nodding along. I appreciated how Bill used simple, practical examples to demonstrate his points (usually accompanied by a verbal wink, just to make sure we saw what he did there).

What does this have to do with data vis? Everything, really. Helping people push past “I want this chart” and get to a good outcome is a struggle we all face. This book is for anyone who needs to work with multiple stakeholders to help their projects succeed. (It might also be useful for stakeholders who need to work with designers, so that they can understand why we’re asking all these questions.)

Here are some of the topics addressed in the book.

Common painpoints:

• Pushing back without saying no
• Stakeholders who dictate solutions or don’t care about their stakeholders (especially the hidden ones)
• Knowing how & when to lose the battle
• Breaking a problem down into manageable chunks
• Switching roles as you moving from problem identification into the design process, and remaining flexible in your approach

What you will learn:

• Using neuroscience and cognitive behavioral therapy to understand stakeholder dynamics
• Keeping the focus on the problem, and not making it about you
• Empathy as a tool to enter the client’s frame of mind, without losing your own
• Creating a space for not-knowing: encouraging curiosity, even when people think they know what they need
• How to prepare for a conversation, and how to use what you hear
• The four components of productive listening: focus, attention, interruption-free, and picking up on nonverbal cues 
• Switching between the surface ask and deeper structure when solving a problem
• Listening for holistic understanding, and simplifying without oversimplifying
• Why finding the right problem might not be enough (and what to try next)
• What success looks like
• How to tell whether your stakeholders are open to whispering, and what to do when they’re not

These topics apply everywhere. I think these techniques might matter more for data vis for a few reasons:

• Stakeholders are less likely to understand the details (of the user task, or the solution)
• Other designers may not have the technical experience to follow along 
• Experts may be so frustrated by trying to explain the problem that they won’t even try. When you can use these techniques to demonstrate understanding, you get to the real conversation faster.

As with all experience, the magic happens in knowing how to dance, not in just following the steps. You need to develop a sense of rhythm and an instinct for where these principles apply. That said, experiment. Apply these techniques. They will help.

Question time with Bill!

I had a few questions after reading the book, so I reached out to Bill. He kindly answered them here, to share as part of the review:

This book was focused mainly on what I would call framing the problem: the needs identification step before you get into the design work. Can you talk about why you chose to focus on that part of the process?

The short answer is that I haven’t seen enough people write about or talk about this. It’s the part of the process that is mentioned but rarely explored in detail. In other words, designers (and others) are told they need to do “needs assessment” or “requirements gathering” and “ask questions”, etc. But to me, that’s like saying “make some beef stew” without providing a recipe. Because it’s not so simple. The recipe is the “how”. You need to ask the right questions, in the right way, of the right people, with the right tone, to really figure out what is called for. And that takes either years of hit and miss experience to figure out on your own or you can learn a process and a way of thinking about this that will get you up and running much more quickly. I wanted to provide that to people based on my experience. Oh, and by the way, a key part of all of this is to first just acknowledge the idea that our stakeholders often don’t know what they need. They need our help figuring it out. Once we acknowledge this, we can move on to the “how”.

What do you do when you’re stuck with a stakeholder who can’t be whispered?

As I say in the book, the short answer is that you should find new stakeholders. If your boss, or client, or whoever, won’t engage, then you should find a new boss/client/whoever. Honestly. Life is much more fulfilling when you’re working with people who respect you and engage with you as a thought partner. That being said, there are some techniques to help soften an intransigent stakeholder. For instance, start small. Just ask ONE key question, like “how will we measure success”, which is a very informative question to help you understand true needs pretty quickly. For instance, if your boss says “make a dashboard of our HR data”, but the measure of success is “employee retention goes up”, then you know retention is a key part of that HR data that needs to be the focus, and maybe it will lead to follow-up questions about how that data might help with retention, what other data might affect it, etc. Part of starting small is realizing you have to gain trust to engage with reticent stakeholders, so a short focused meeting with incisive questions will earn you longer and more complete conversations over time.

Designers are often very good at listening, but struggle when it’s time to transition from a position of understanding to become the expert presenting solutions. It can be hard to be seen as an expert when you’re in the role of listener and learner (especially working with an experienced team). Can you talk about ways to avoid this trap?

Expertise is an incredibly valuable thing. If you are new in your career, you may not be perceived as the expert, which makes things harder. But the great news is that you can lean on others’ expertise. Rather than saying to your stakeholders something like “pie charts suck!”, you can say, “we know from research on human visual perception that humans aren’t very good at distinct value comparisons when looking at circular shapes, so a pie chart won’t be as effective for this visual because you really want your audience to compare those two numbers – research also shows that a bar chart will be much more effective here, so I’d recommend that.” When you cite research, that glow of expertise will shine on you and you will gain trust. As you gain more and more trust, you will eventually be perceived as the expert and you will walk in the room with the gravitas and respect you need to engage effectively with any stakeholder!

Interruption free can sometimes be a problem for time management when talking to an expert. Can you share some techniques for using active listening to guide the conversation, as opposed to giving up control?

There is a fine line between active listening (really listening and hearing everything, without jumping constantly to your own thoughts and reactions and perceptions) and simply being someone’s audience, and they’re driving the entire conversation. The difference between the two is a true dialog where you are asking good follow-up questions based on what they’re saying. BUT, the key to doing this well is to NOT be perceived as just listening so you can jump in and respond, which is what most people do, right? (Listen, react…listen, react…) No, you need to truly listen, really hear what they’re saying. What they’re saying will trigger thoughts and reactions in you. Capture that if you need to. And respond with questions. But probably not every thought and question you have needs airing. What are the ones that you really need to address in the context of helping your stakeholder figure out what they really need? This is a gray area and something you can only learn over time and in your context, so this is something I can’t exactly teach, except to suggest you try to find that balance. Simply being reminded that there is a balance to be found will hopefully help you get there in time.

You discuss the importance of building a holistic understanding of the problem, and switching between superficial and deeper concerns. Can you talk about how to interpret what you hear, and how to process that interpretation with stakeholders?

One of the most important initial ideas in Stakeholder Whispering is to acknowledge that we live our lives driven largely by our subconscious. So in the context of work, that plays out in the automated response to all of our work. For instance, in today’s world, what do we do when we want to make “data-driven” decisions? We measure stuff, and then we make a dashboard out of it! This automated response isn’t bad, but it’s just so rote that we don’t always think it through. We need to measure stuff, but which stuff, and how much, for how long? And we need to understand that data, but is a dashboard the answer or might it just be a 5-minute call to review one key metric? It depends. So we have to probe deeper than the automated response. This applies to everything. So to the question, the “superficial” is the initial obvious concern/request/plan. And “deeper” review is literally the entire point of Stakeholder Whispering. Sometimes the superficial initial idea may be all that’s needed. But sometimes it isn’t. Whispering to figure that out is what it’s all about! The way to do it is to ask incisive questions, open your ears with your domain and data expertise, trust your gut about things that you know might be concerns or worth further exploration, and probe those. The book is full of specific techniques to do it, and it’s hard to explain without diving deep. But the short answer is simply to engage what I call “useful paranoia”. Something is always missing or not quite right, so probe it! But that doesn’t mean everything requires a deep rabbit hole. Explore thoughtfully, and know when you’ve done enough to move on to the next concern. This is also something you will develop over time, but hopefully the ideas I share in the book will speed up that process.

For a new researcher, it’s often hard to balance best practices from the quantitative social science research they might have learned in school and design research in a business setting. Concerns about deviating from script, “biasing” responses, etc. are common. To me, it’s always been a matter of incorporating those best practices into a more fluid dance of the conversation. Can you talk more about how you think about that balance?

I think that balance is actually inherent to the Whispering process. Because the way I recommend doing it (and I talk about this in the book) is like therapy. When you go into therapy, and you share your childhood trauma or relationship troubles (or whatever), your therapist doesn’t give you solutions or ask leading questions. They ask intentionally open-ended questions like “how does that make you feel?” The point of therapy is to help you understand what you’re feeling. That’s what Whispering (and research) is about. You ask unbiased questions to be sure your data is pure. Now, in Whispering (as in therapy), sometimes the questions will eventually start to lead the witness a bit. The therapist may eventually say “it seems like you’re getting angry…is that what you’re feeling?” because they are there to guide their patients to some degree, based on their expertise. And in a Whispering session, you may start to ask less open-ended questions as you get a sense of where things are going. You might start with something like “why do you think a dashboard is best for this project?” But later in the conversation, you might ask something like “do you think a report might be more effective since you mentioned that people will be reviewing this on a plane and only 2X per year…maybe a dashboard isn’t the best tool for the job?” It’s OK to get to this point because, as the therapist, using your expertise and experience and active listening, you can help guide your stakeholders to the best decision based on the conversation. You’re not conducting primary research, so the standard does shift a bit from those types of conversations, and that’s the “dance of the conversation”, as you describe it, that you need to get comfortable with.

The post Review of Stakeholder Whispering by Bill Shander appeared first on Nightingale.

Not many people know this, but I had a protracted search for work a little more than a decade ago. Eventually, I did land a job. And after I got it, I leaned into the process of hiring others so that I could learn how to build a team and get jobs in the future. Since then, I’ve hired—or helped to hire—dozens of people of various skills and job tenure, so I have a good understanding of what hiring managers are looking for, and how the process works.

Searching for work when you know you have the skills—but may not understand the right approach to get the job—is difficult in many ways. More than anything, preparing to get work, and then getting the work, is a process. Understanding the system will help you navigate the ambiguity and help you (try to) remain calm. Let’s look at an average hiring process so that we can customize our approach for each step.

What are people looking for?

For the job to open up in the first place, the hiring manager may have had to fight to get the headcount or maybe the organization identified the need and is creating a new role. Regardless, a role on a team usually starts long before you ever get involved—it’s good to keep this in mind. 

Usually, a job posting is the next step. This is a short description of the ideal candidate—a “unicorn” who can do everything from hardcore tech skills to nuanced skills in communication and management. Everyone knows that finding the right mix of skills may not be possible; but a job posting is, essentially, fishing for talent—and employers never know who will bite.

Getting past the AI filters

From there things take a bad turn. The rise of AI means that every resume is scanned by a bot for keywords and given a “job-fit score.” The highest-scoring resumes are then passed to a recruiter to review. That reduces the pool of applicants from hundreds to just a handful – it’s brutal. But when a real person finally looks at your resume, they check for matches to the skills. If you have a good hiring manager, they will look for, and explore, a portfolio of work you have linked from your resume. Having a portfolio is your best way to differentiate yourself from the competition.

We live in a time where traditional higher education faces challenges in validity from tech bootcamps and professional training. This reduces the need for a brilliant resume because showing people what you can do is a more direct way to help them understand your skills and it’s more immediate than a description of your academic or professional career.

Unfortunately, you absolutely still need a resume (I’m sorry!) to pass the bot test. Do yourself a favor and make your resume as minimal as possible, filling it with keywords that will connect to the skills in the posting and nothing more. Can you do data engineering? Check. Have you used Tableau? Check. Have you designed in Figma? Check. Your resume is for the machines, so make it unimaginative and as easy to read/scan as possible. Create it as text only. Elaborately designed PDFs are usually skipped, so don’t waste your time—spend that time on your portfolio! 

It’s astounding to me that so many people in data viz don’t have an online portfolio. Data viz is something seen and experienced, so showing people what you can do is essential. 

Your portfolio should do a few things: 

1. Show your published work, or sanitized examples of what you have done.
2. Demonstrate your skills and abilities in design, data, and data viz.
3. Walk through your process: how do you understand data and collaborate with others?
4. If possible, demonstrate who you are as a person. Are you curious? Creative? Do you have a mission? Are you fun? Prove it!

Congratulations, you got an interview!

Next come the interviews. Every organization has a different approach, but they all have a few steps. The first interview is a screen for team fit and skill check. They are trying to answer two questions:
(1) Is this a person I could see myself working with?
(2) Can they do the job?  

If you pass the first interview, you are often connected to technical colleagues to ask more detailed questions. They may be probing for engineering skills, a design review, or checking with other managers or team leaders. Once you pass those, they may want to conduct a test. This can take the form of an in-person coding review to an assignment to be done at home. After that, there’s usually one last interview with the big boss. Let’s get into the details on how you can optimize for each step.

The people interviewing you are likely sandwiching it between other meetings and may only be giving you a fraction of their attention. Your job is to get their full attention—to grab them and make them curious to learn more. One way to do this is to come with your own questions. Ask questions to your interviewer about the team, their skills, process, collaboration, timelines, and culture. This signals that you are curious and proactive. 

As you progress to the next round of interviews, be sure to take notes and respond personally to everyone using their names. These are your future colleagues and you want to build rapport from the first day. It’s crucially important that you take a collaborative mindset even in the interviews. If someone asks you a question that you don’t know—say you don’t know it and that you’ll follow up afterward (and you better do it, too). This shows you can be trusted and aren’t full of hot air.

If they give you a test, try not to get too stressed. Consider it more like a collaboration with professionals and try to have fun. Yes, they are looking for you to demonstrate what you know, but are also looking to see how you work. Try to keep your sense of humor. If you don’t know something, use it as an example to explain how you learn on the job. Remember, it’s not just a test of skills, but of how you react to situations.

When you get to the last round interview and you are talking to the big boss, this is mostly a formality, so take this opportunity to learn about the vision and direction of the organization. Try to get them to open up about where they want to lead the group, and if they do, it’s an opportunity to help them accomplish their goals after you get hired—it’s like insider information for your future team. It also shows that you are ambitious and goal-driven and that you care about the trajectory of your new team.

Game the system

Once you understand the process, and have empathy for the people behind it, you instantly get some sense of how to optimize for each step. Be prepared, so try to focus on what you can control and let go of what you can’t.  Keep in mind that it’s a numbers game—apply to as many positions as you can. When you have interviews, make them count. The process of getting a job is both impersonal and deeply personal—understanding this is a measure of your professional maturity.

Looking for a job is super hard—trust me, I know—so try to keep your emotions in check. It’s easy to get worn down by the process so focus on who you are. Put your energy into your portfolio, your passion projects, and be prepared for the interviews. Get plenty of exercise, try to take the weekends off, and spend time with friends, family, and your professional network. I’m always amazed by how many people want to help—let them! 

Looking for work or hiring? Check out the Data Visualization Society’s Job Board.

The post How to Get a Job appeared first on Nightingale.

Can you introduce yourself, Jo?

Of course! My name’s Jo, and I’m a designer and writer based in Melbourne, Australia, originally from Indonesia. I completed my studies in design at RMIT University here in Melbourne, and since then my passion has been towards furthering storytelling, technology, and design for education and community. Ultimately, it has led me to start and build the Data Garden Project!

What is the Data Garden Project? Can you tell me more about it?

The Data Garden Project (DGP) is a free resource and learning community that introduces young people to data visualisation and creative coding. It explores using data and code as a storytelling medium—to share data-inspired stories about your own life with your peers or loved ones.

The idea for the DGP came as a Capstone graduation project when I was finishing my design degree. I took a creative coding unit during my final year where I created a data viz artwork of WhatsApp conversations between my mother and I while we were separated during lockdown—visualised in a garden metaphor. I titled it A Garden of my Mother’s Concerns.

I was intrigued by how the creative meets the generative in the process of coding, but also how data and code can be used to convey meaningful, personal stories that we can share with others. So I decided I wanted to pass on this same experience and learning opportunity to others—especially fellow girls or young women from non-computing backgrounds, like me!—to learn creative coding with me as a new way of visualising data stories.

That’s an interesting concept. How exactly do you do that?

By embarking on a “Data Garden Project,” you’re invited to find and gather data from your life or surroundings—this can be the food in your pantry, music in your playlists, interactions with family or friends—and then we visualise that data using the basics of drawing with p5.js, a beginner-friendly JavaScript creative coding software.

A big precedent or inspiration for the project is Dear Data, created by Giorgia Lupi and Stefanie Posavec, a year-long analogue “data journaling” project as a way of sharing, with each other, data stories from their own lives through sketches on a postcard. Replacing pencil drawings with p5.js canvases, our project uses coding as the storytelling medium. I guess you can say it’s part coding class, part data “treasure hunt”, and part collective journaling activity.

What has the journey been like for the project?

We were awarded our first creative grant from the Blackbird Foundation,a VC based in Sydney, in August 2021. When I got accepted into the grant program, I was part of a design student club at my university, and I did a call-out on Facebook asking if anyone would be interested in joining me on this “mission”—which at the time had not taken any form! 

Luckily, I met four wonderful peers and recruited them as my first team, and we quickly became dear friends. We took to Discord and “beta-tested” the Data Garden learning material by running two to three “Team Tutorial” sessions every week, where I would tutor them and we would go through each module and exercise via a group video call.

Our six modules combine the basics of creative coding with the basic principles of data storytelling. By the end of the modules, each student creates a final data-driven art project made with p5.js, representing a story or theme of their choosing. At the end, it felt like a breakthrough. One of our team members, Kelly, created a flower grid visualising the songs she listened to during lockdown. I thought, Whoa. The Data Garden Project works!

In 2022, I began to record each module as bite-sized tutorials, which now exist on our YouTube channel. It helped us gather some more audience around our mission, which was to make creative coding and data visualisation accessible and enjoyable.

The community grew in our social media and Discord—we recruited new team members and hosted ‘Study Spaces’ on the weekends, where we would open a room on Discord for an hour, and people could come, relax, chat, or use the space to do some work. We also began hosting a few online workshops on Miro. In March 2023, we hosted our first in-person creative coding workshop in partnership with RMIT University, and it was a blast.

In June 2023, we got accepted into the Processing Foundation Fellowship Program, which felt surreal. With the help of the fellowship, we were able to work on creating an educational resource and guidebook not just for students, but also for educators to take and adapt the Data Garden material to their own classrooms and communities. The free learning resource comprises six modules, combining the basics of creative coding, data visualisation, and exercises on building narratives and stories woven with data. Its purpose is to guide young people—with a focus on young girls and women—to create their first-ever data-driven interactive artwork using code.

What was the need? How did the Data Garden mission come about?

I initially learned creative coding in 2020, and, so, due to the state of the world, I had to adopt this brand-new tool in isolation. I’m grateful for my lecturer at the time, Karen Ann Donnachie (now a mentor to the DGP), who provided us with a warm, supportive learning environment even while remote—especially since learning to code is such a big, daunting task. That played such a big role, so that was the first thing I wanted to pass forward: a space that was warm, collaborative, and encouraging. The need, firstly, was to create an environment that takes the loneliness out of learning. 

Often, I felt that I don’t always find this in online coding classes or data bootcamps. Where there’s a lack of community and a sense of play or peer-to-peer support, you embark on an individual upskilling pathway with the mindset that the world is your competition. As best as we can, we want the DGP to offer more of a playground or collective sandbox for everyone to learn, fail, test, win, and try again together, with a peer-to-peer learning approach rather than instructor-to-class. What one learns, everyone learns.

The second need was to demystify gender biases around STEM. We know statistically that women comprise only a little over 30% of the STEM workforce. Moreover, girls and young women are also outnumbered in STEM-related majors in school or college and are less likely to pursue them than their male peers. These gaps are further aggregated in communities where the infrastructure for technology is lacking.

The overly militarised and commodified end products of technology and software can also add to this bias—when we think of science, subconsciously, we may think of weaponry, vehicles, video games, the rise of AI, which are traditionally coded as masculine and can feel disconnected from practices that feel arts-based, grassroots, or close to a community.

Through the Data Garden, we wanted to explore coding as if it were a fun scrapbooking or art activity that you can do after school, engaging communities of women to give that sense of belonging, and to show that this is a space for them, too. We turn software into something crafts and knowledge-based, lowering the barrier while providing an avenue for connection and vulnerability through story-sharing, not just upskilling.

Finally, the project-based nature also reduces the feeling of being overwhelmed when learning data and coding. A survey we ran back in 2021 within our community found that most of them felt overwhelmed by “too many coding languages.” So our modules are very introductory and straightforward—students learn JavaScript through the p5js library, a bit of HTML and CSS to create their webpage, and that’s it. As for the data, we’ll learn how to gather data, place it into a spreadsheet, how to read visualisations, and create one of your own. The goal remains as one output: A data-driven artwork to live on the web that tells a story about your life—and that simplifies the learning objectives.

And the Data Garden Project is now community-focused, is that right?

Yes! We host our community on Discord as a central hub. We host online workshops using Miro, the team does our brainstorming sessions here, or sometimes we like to open a one-hour Study Space on a weekend where we put on lo-fi music, chill, and just work on our own things in the company of others. Most of us are based in the Asia-Pacific, but as the Discord community grew, there was also a time when we opened multiple Study Spaces to accommodate those in Europe or the US.

We also publish much of our content (such as modules, project updates, or inspiration) on Instagram and YouTube. One of the things we did on YouTube earlier this year was host Sharing Sessions, where we sit down and do Q&As with data viz or creative coding practitioners in the field, who share about their work and career journeys. 

The Data Garden Project challenged my notion that dataviz is always clean-cut and clinical as seen in most scientific publications, but turns out it doesn’t have to be that way!
—Septia, a member of the DGP

What are some things you noticed or learned while you were growing the Data Garden?

It was a big surprise to me to see community members trickling in from different parts of the world. At first it started with close circles of peers in Australia and Indonesia who were interested in what we were doing. All of a sudden we saw introductions from the UK, Philippines, USA, India, Mexico, Peru… I thought, We’ve never even been there!

At our core, we’re more of an international “study club” that learns creative coding and dataviz together. We partner with educational organisations or institutions, but we aren’t representative of any specific one. And I think that has kept the project approachable, malleable, and accessible for people to enter into and rally behind.

I was also nervous at first about starting the initiative with no computer science credentials (unless we count self-taught through p5.js YouTube tutorials). However, it was a delight to learn that our offering of community, storytelling, and creativity is what draws people into the DGP. This shed a lot of pressure from having to be like a coding course or bootcamp, and gave us more confidence to play to our strengths.

Speaking of confidence, that’s probably the biggest, most heart-moving thing I’ve seen happen since starting the DGP. We have team members scattered across a few different pockets of the globe. (It’s quite something to have to arrange meeting times between four or five different time zones!) When our Melbourne team finished our first offline workshop this year in March, a couple of members who were based in Jakarta started shooting their hands up: We can maybe do a similar workshop like this here! A team member based in the US also said: I found a space that can be great for a Data Garden workshop. Maybe I can run one here?

Moments like these, to me, are small seeds of potential for what the project could be. Not just for us to be planting seeds and growing our own little garden, but for it to take root and grow someplace else, adapt to new communities and be implemented in different ways. The baton gets passed on, and that’s what I’m hoping our future consists of. That it lasts far beyond us.

Speaking of the future, what are your future plans for the Data Garden Project?

Right now, it’s about making sure that this project’s legacy lives on. We created our guidebook resource on Notion earlier this year, and I’m very excited for it to be an evergreen resource for computer science educators and creative educators in all parts of the world.

The resource houses our six learning modules, combining written guides and our YouTube video tutorials. It includes a mix of coding challenges, storytelling or writing homeworks, simple data visualisation concepts and examples, and workshop or activity ideas for the classroom. For educators, we also include slides or materials for class settings, as well as tips on how to facilitate a workshop drawn from the Data Garden.

I like to say that the resource is complete, but will always be iterative. There are so many ways educators can enrich it—either from a creative coding or a data visualisation perspective. For instance, we can discuss deeper about the level of data literacy that’s needed to engage in the Data Garden course. In Module 4, we explore how to gather and parse data, and there’s a section there about “Treating Data with Care”, where we are invited to discuss biases or incompleteness in data, and educate about the power dynamics in data analysis and visualization.

In 2024, I would be excited to see how the Data Garden Project can be implemented in a classroom cohort. There’s a lot more space to build community within the timespan of a semester, a summer camp, or a curriculum.

Last, but not least, we also have an exciting summer workshop in collaboration with MPavilion Melbourne this coming February—a beautiful architectural space that sits just south of Melbourne’s CBD. We’re calling it the Data Stitching & Storytelling workshop. Like the name suggests, we will explore how to visualise nature and our surroundings using cross-stitching or embroidery, bringing back tactility and crafts as a way of communicating data and introducing computational thinking, so we’re very excited about that.

What do you think an initiative like the Data Garden Project enables?

In our first Team Tutorial sessions, some of my favourite moments were when my peers would show me and the team the coding projects they made over the weekend, going into the process of how they made it and teaching the team how to create the same.

With every cohort, there’s always the expectant hope to see our learners become teachers in their own right. When we talk about this garden taking root and growing elsewhere, it’s really about young people being able to lead, and share their knowledge through community-led learning. 

Whether that’s sharing about their finished projects on socials, speaking about their work, explaining their processes, and—once they’re ready and willing—passing that knowledge forward, either through creating new learning content with us or running workshops or after-school DGP clubs on their own. I imagine it as this “mitosis” of learning, and I hope to see more of it in the near future.

Arshi, a member of our community from Kolkata, for example, is in the process of building new YouTube tutorials for us, this time a crash course on Tableau, drawing from her professional experience in analytics. We’ll also be looking at ways to equip our team more to facilitate workshops and share Data Garden material. Diversifying our learning content and giving our community the open space to experiment in those ways will be exciting.

That’s splendid. Lastly, where can we find you?

If you would like to partner with us and chat about our resource or community, we’d love to be in touch. You can reach us at datagardenproject@gmail.com and we’re always open to collaborate. 

• Instagram: @thedatagardenproject
• Youtube: @datagardenproject
• Discord: bit.ly/JoinDataGarden
• Access our resource: bit.ly/DataGardenGuide
• Website (to see students’ works and read about our team): datagardenproject.com

Want to get involved? We are currently seeking teachers or education partners who would like to collaborate with the Data Garden Project to pilot this resource inside their classroom or community, or adapt it to their existing lesson plan for the new school term. Reach out to us at datagardenproject@gmail.com to get in touch.

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Under her art direction, the final design—which stretched across the entire front and back page of the paper—won gold medals from the Society for News Design in multiple categories including “Story Design,” “Front Page,” and “Combination Print & Digital.” Carrie, herself, also won gold in the “Individual Portfolio” category.

In the following conversation, Carrie talks through the process of capturing one million deaths in print media. This interview was first published on “Newspaper Design.” It has been lightly edited.

How did you conceive the award-winning “One Million: A Nation’s Immeasurable Grief”?

When I was asked to do this, I knew it would have to have news value, be informative, be sophisticated but also carry the weight of the loss somehow. I was looking for a way to do all of that.

I heard the graphics editor Jeremy White, who was working on this, already had ideas. The dots concept was his—I just saw it and saw some of the shapes the dots might take. When I saw the United States formed by the dots and each of them was a person, I knew that was the image. It was informative, emotional, and simple, while also being very complex. It did a lot of work without overwhelming the viewer. Adding the annotation that each dot was a human was something I felt got to the heart of the image, so I knew we’d need to include that somehow.

There was talk of making this front page bigger than the others to make the statement that this was a historic moment. So my managers, Fred Bierman and Andrew Sondern, and I wanted to make it a wraparound page one if we could, though we knew it would be a challenge.

Was it even possible? We needed to see a print version of the graphic. Then we needed to figure out how it would print best on newsprint. We really wanted the dots to print well. This was very complicated and involved trying various dot sizes and ink combinations (grayscale and black & white) and opacities. We also had to figure out whether it was worth adding outlines for the country, states, counties, etc. We actually did an eight-page press test to make sure we were doing the right combination for print so that it would be readable. After that we just had to wait until we were closer to the date to finalize the map with the latest data.

“We really wanted the dots to print well. This was very complicated and involved trying various dot sizes and ink combinations and opacities.”

At the same time we had to convince editors at The New York Times to just run this on the front page and clear the back page of ads for this paper. One thing on the front page of The New York Times?! And it’s not even a story start. But once people saw this, they started to get on board. They found it as moving as we did. Tom Bodkin and Tom Jolly were very helpful in helping us move forward.

Then at last, there was writing of the display type, which took a village. But I believe where we landed was right.

It was a sleepless night both before and after this went to press.

How have readers responded to that unconventional approach?

We never do something like this for page one. Never. Readers took note. Of the responses I heard, people found value in this treatment in remembering loss and recognizing what we, as a nation had been through. That meant a lot to me, because that’s why I do what I do.

“We never do something like this for page one. Never.”

Which software have you used to make it?

I know our graphics team has special software to gather and analyze data, and they had been gathering since the start of the pandemic. For me, to make sure it would print well, I placed the high res .tiff I received from Jeremy into an InDesign document with the single dot annotation and the display type and exported it as a pdf with our press settings to insure everything would stay together and print well. Then I had to place onto the live pages in Newsgate as a single image. But with the normal page elements (like the NYT flag) undisturbed in our system.

This page won so many top awards in different categories of the SND competition. Why is this work striking from your point of view?

First, I just want to say that I am so honored and proud of this work receiving recognition.  Everyone involved worked very hard and with great care. I think it’s striking because to me, this feels like more than a newspaper page. This feels like more than a layout. This feels like a moment of recognition and memoriam that we visualized.

“This feels like more than a newspaper page.”

That is something that I didn’t know was possible but that is how I see this page. This feels like more than a layout. This feels like a moment of recognition and memoriam that we visualized. That is something that I didn’t know was possible but that is how I see this.

As an artist, what is the emotional feeling that passes through you when doing this?

I’ll be honest, I cried a lot working on this. Ans I’m okay with that. This was such a massive loss and I think really letting myself feel that, lets me know that I’m doing work that is honest and has depth.

To whom are you going to dedicate this award?

To the readers and the future readers (two of which are my kids). Journalism makes the world a better place, and I’m glad I am still a part of that effort.

How do you approach your projects?

I always ask myself what’s the core of this piece? What makes it special? What does it need to convey? And I let that inform how I envision it in print. Then I like to talk with any editors involved, make sure I know everything I should know and am thinking correctly about the project. Then I usually start to write ideas, words, concepts, and then start drawing pages. That translates to the computer and then eventually to the page.

How do editors and artists work closely in The New York Times?

It depends on the team and the project. I think my best projects have been because of good communication and collaboration with editors. I think every project needs to be about the story first and if you can’t work closely with an editor, you might be missing out on ways to elevate a piece in print.

Do you think AI is going to create a revolution in the field of data visualisation and information graphics?

I think it will be a great asset but not a revolutionary tool. You still need a human to evaluate certain parts of projects, be sensitive to tone and nuance. I’m also interested to see how news organizations will keep readers trust and maintain editorial excellence as they begin to use these tools.

The post Capturing One Million Deaths on a Page: A Chat with NYT’s Carrie Mifsud appeared first on Nightingale.

The book is structured along the four different stages of data analytics: descriptive, diagnostic, predictive and prescriptive (see Figure 4.1 from the book below). Using the example from the medical profession, Morrow refers to descriptive analytics as a doctor telling you what your symptoms are. Diagnostic analytics takes it a step further and focuses on why your symptoms are occuring. Predictive analytics reflects the medical profession’s research, which tells the doctor which treatments will lead to different outcomes. The final stage of prescriptive analytics is when the doctor would prescribe you with medication to treat the symptoms. Just as with a visit to the doctor, this process is rarely a linear pathway and is often an iterative process where earlier stages are revisited as part of the analysis. 

Rather than providing technical instruction or code recommendations, Morrow focuses his book on providing a high-level framework for how to understand the key questions and value from each of these stages and how it relates to different types of occupations from data analysts to data engineers. Through weaving in both personal and professional examples, the book strikes an effective balance of providing a clear foundation for anyone new to using data while also highlighting critical insights that will be valuable for more seasoned experts.

While the book is primarily focused on data analytics, Morrow also weaves in discussion on data visualization throughout. One key quote from the book related to data viz is: “Let’s remember that data visualizations are an important part of data and analytics, but they are not the end goal. The data visualizations should be there to help end users get the insight they need to do their jobs better” (pg. 60). Within this framework, the book provides helpful recommendations on how data visualization can enhance the analytics process while maintaining a clear focus on the bridge between data and decision-making so that data viz is a value-add rather than a superfluous distraction.

This book provides a clear focus on the ultimate purpose of data and how it can be useful in driving decisions. I often fall into the trap of assuming that making a data analysis or visualization more technically complex will naturally lead to it being more valuable. Morrow does a great job of deconstructing this mindset and focusing on how different parts of the data analytics process from initial descriptive analytics to more complex prescriptive analytics all have a critical function to play in driving decision making. If you are interested in having a strategic framework to guide how to use data better in your professional and personal life then I would highly recommend giving this book a read.

To learn more about this book, I had the chance to interview Jordan Morrow to ask several questions. See a synopsis of that conversation below:

Joshua Pine (JP): Could you give us a brief introduction to the book from your perspective? What do you see as some of the key insights?

Jordan Morrow (JM): I don’t think I would have ever thought I would write three books and am now writing a fourth. For this book I wanted to continue on the trajectory from my first book which was focused on data literacy and focus on the world of data and analytics. For most people, they don’t need another book about formulas or statistics. I wanted to weave in more than just business examples and share personal anecdotes which people can relate to more. I want people to see themselves in the world of data analytics and provide a conceptual framework. 

JP: What do you see as the overlaps between the worlds of data analytics and data visualization? How can data viz specifically work within the four stages of data analytics (descriptive, diagnostic, predictive, and prescriptive)? 

JM: When you have a dataset to analyze with 50 columns and 100,000 rows, you don’t want to have to manually look through that to find insights. Data visualization is a powerful tool that can spark curiosity, questions, and discussions around diagnostic analytics. Visualizations can bring these analytics to life and can also be part of later steps in the analytics process including predictive analytics models.

How data visualization plays out within the four stages is highly dependent on context and needs. While Excel often gets a bad reputation, it is often sufficient for a lot of visualizations. Sometimes we’re just focused on what is happening and our data visualization can stay within the descriptive analytics space. Other times as we grow in data literacy, we may need a self-serving dashboard that targets the diagnostic or predictive analytics stages. As we know, our visual perception is often the most powerful and when that is harnessed as part of prescriptive analytics or generative AI tools it can help illustrate some really complex topics.

JP: How does data storytelling and data literacy intersect? How can you craft data visualizations that both meet audience members where they are at in their literacy journey while also pushing them to grow and mature?

JM: First of all, we need to get to know our audience members really really well. After that, we need to explore how to integrate education into data visualization, whether that’s in the form of tooltips with additional information or links to guide the user through the process and explain any new or foreign concepts. Another important piece can be to find an accountability partner to bounce ideas off of and to gut check whether the visualization you are creating accomplishes what you’re trying to get at. 

JP: In your book, you emphasize the “human factor” that can foster creativity and contextualize our analytics work. How do we balance the positive aspects of our human contributions while avoiding the dangers of bias?

JM: With the rise of generative AI tools, it is more important than ever to lean into the human element of our analytics and visualization work. As more mundane and routine tasks get automated, we should embrace our creative human side to shape the direction of those tools. In order to do this well, however—while minimizing the dangers of human bias—is where data literacy comes into play. Continuing to grow your data literacy will enable you to better understand what type of insights you’re able to derive from the data and where potential biases may emerge. Another strategy that can help is going back to an accountability partner or mentor who is able to provide candid feedback based on mutual trust and respect. Finding someone in your life who can fill that role can be really powerful in your data journey.

“With the rise of generative AI tools, it is more important than ever to lean into the human element of our analytics and visualization work. As more mundane and routine tasks get automated, we should embrace our creative human side to shape the direction of those tools.”

JP: How should we view the shifts that will happen in the data analytics and data visualization fields due to generative AI? How can practitioners prepare themselves for this new reality?

JM: The reality is that generative AI tools will be disruptive and will inevitably lead to job displacement or, at the very least, it will replace certain tasks within job portfolios. We should embrace this new reality and recognize that it will free us up to engage in deep work and focus on our creative human potential. We should view generative AI as our partner and leverage its technical capacity so we can flourish as data analysts, data scientists, data engineers, and other roles. We should compete with, rather than compete against, these tools.

With regards to the four stages of data analytics, generative AI seems best suited to support the descriptive, predictive, and prescriptive processes. Based on its current capabilities, it does not seem able to fully fulfill the diagnostic phase with a focus on deciphering insights and answering key questions regarding the why behind observations. From a career perspective, it seems that that diagnostic phase may be most valuable for us to lean into right now as we continue to partner with generative AI tools. 

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The post An Interview with Jordan Morrow, Author of “Be Data Analytical” appeared first on Nightingale.

Jaime Tanner: Information designer and developer who became the first accessibility visuals editor at The New York Times last year. 

Johny Cassidy: BBC journalist who recently pivoted his career to focus on making visual data journalism more accessible. He is also registered blind and relies on screen readers. 

Is the data viz world ready to embrace accessibility?

Johny: I would argue, and I have argued, that data viz is an accessibility tool in itself. Creating visual data is taking raw datasets that normal people wouldn’t really understand—those raw data sets are just numbers, numbers, numbers—and making it into something that’s accessible, for want of a better word, because the human brain is made to look for patterns. So it’s not really a massive leap, then, if you do understand and agree that data viz is an accessibility tool, to go one step beyond and make it accessible for everybody. 

Jaime: Agreed. If you’re working in this field, then you probably got into it with the desire to help people to understand the world, to see the forest from the trees, or to envision something that might otherwise be difficult to imagine. To realize that, in fact, the data viz that you created to make information more accessible might have actually created a barrier to access for many disabled readers—I think that revelation is jarring. So I do feel many are eager to learn more and to think about what they can do to make their work more accessible. 

Johny: I think in theory, they are. Everybody’s up against time and resource issues. So although there is an understanding of why it should be done, the fact that the processes aren’t as easy as they could be to make that happen is also jarring. 

Jaime: Yeah, I do think that’s the reality. It’s a big shift in every step of the process. There are technical challenges. There are new skills to learn. And, of course, many are unaware that there is a gap to bridge. 

Tell us about the progress you’ve seen and whether you’ve noticed tendencies to backslide.

Johny: Something that I’ve seen a lot more is the “shift left” practice, to get accessibility provisions baked in from the start, so you’re not trying to retrofit at the end, which causes everybody headaches. Having people with an understanding, experience, awareness, and willingness to be the person in the room to say, “Okay, this sounds really good, but what about the folks who need accessibility provisions”—that’s becoming more and more prevalent in a lot of the work that I’m seeing. 

“Something that I’ve seen a lot more is the ‘shift left’ practice, to get accessibility provisions baked in from the start, so you’re not trying to retrofit at the end, which causes everybody headaches.”

Jaime: The challenge is to make processes sustainable, so that accessibility is something we’re considering every single time we make a product or graphic. When you are working on something pressing that needs to be completed today, you have to make decisions that allow information to get out quickly. You need to be able to fall back on a workflow that ensures the accessibility of your piece doesn’t fall by the wayside. So much of that, in my opinion, comes down to making this work a part of your practice. If you regularly make sure to include text descriptions, or alt text, in the data viz that you create, for example, the work becomes much easier. You know how to add it to your projects, you have practice writing it—all of that helps to make it an essential part of your work, even on a deadline. 

Johny: Right, and I think that became really, really obvious and prevalent during the pandemic. A lot of big organizations and news broadcasters use data viz, and they can do accessibility stuff day in and day out, but when the sh*t hit the fan, accessibility was the first thing that was dumped because things had to be done so quick, the COVID dashboards had to be done so quick. I did research at Oxford University that showed that accessibility was not in the forefront of people’s minds when they were trying to get out that vital, crucial information. So you can really see the gap in that time-pressured workflow and how it was being considered. So, I think—I would hope—that there’s going to be lessons learned from that. 

What’s driving the change to consider—and practice—accessibility? 

Jaime: It’s an interesting question, what has sparked the change. There are more folks in the data viz community talking about accessibility and sharing examples of what’s possible. It’s perhaps hard to understand how far behind you are until you get an example of what an accessible graphic could be. Once you acknowledge that there is a gap, it’s hard to ignore it. 

“It’s perhaps hard to understand how far behind you are until you get an example of what an accessible graphic could be. Once you acknowledge that there is a gap, it’s hard to ignore it.”

Johny: In fact, I think it’s something a lot more simple: It comes down to the proliferation of competition. It’s a cold, hard business case. When there are so many different publications fighting for eyeballs, fighting for readers, and fighting for viewers, there’s a case to say, okay, we are actually going to make a big commitment to serve audiences that perhaps haven’t been served as well as they could have been in the past. Steve Jobs and Tim Cook did this with the iPhone at Apple. They were the first to provide VoiceOver [Apple’s built-in screen reader], and that baked accessibility into a phone operating system from the outset. And it wasn’t really because they were nice guys. It was because they saw that cold, hard business opportunity to find a market that hadn’t been served. It created brand loyalty, and I think there’s something similar happening now. At the BBC, the way the funding model is, we have to have value for all, and there are a lot of disabled people that pay the license fee and you can’t forget about them. 

It also comes down to diversity and inclusion. You know, a lot of really good thought leaders understand why it’s vital to not just be taking from the same pool all the time. If you want to revitalize your workforce, you have to have people from different backgrounds, different experiences—and that’s including disability. Disabled people might have been invisible in the workplace before. As people become more aware of colleagues around them, it becomes normalized. And when people with different accessibility needs are more visible in society, then they’re more forefront in people’s minds when they’re designing and building products. 

Based on your experience helping others integrate visual accessibility into their work, what is your advice to people who need some guidance?

Jaime: Learn how to use VoiceOver, and open up a few projects that you like in a web browser, and you’ll hear immediately what information you’re not getting and what kind of artifacts are announced that you don’t necessarily need. Hearing it yourself—it’s immediately impactful. 

Also, get over the fear of doing it wrong. I have this sign on my desk that says, “Do it badly,” which is just a little reminder to myself to get out of my own way and try something. Every time you’re able to take even one step towards a more accessible solution, you’ve made something that is more inclusive—the work that you did is something that you can improve on and learn from, and that over time can become a part of your practice. 

Johny: I would say, don’t be afraid to reach out and ask people who have experience. You know, not long ago I was very new to the whole community of data viz practitioners and people that were working on accessibility. And what I found is that it’s a really welcoming, inviting, supportive community. And everybody is there for the same reasons, wanting to push for the same goals, and making things a lot more accessible. 

This article originally appeared in Issue 3 of Nightingale magazine. Get your copy here! 
Looking to make your data visualizations more accessible? Check out our resources page!

The post Making Visual Accessibility Part of Your Data Viz Practice appeared first on Nightingale.

Lalena Fisher is a multi-talented creative with a diverse background in journalism, painting, graphic design, and music. She has designed for shows like Blue’s Clues and The Wonder Pets, created information graphics for The New York Times, and written and illustrated children’s books. Lalena also has a career in music, including a mother-and-daughter heavy rock duo, The Mothermold. Her work reflects her fascination with dichotomies in life and her desire to challenge them. Through her art, she explores themes related to family, gender, strength, and dominance, shining a light on the absurdity of assumptions about all dualities.

Lalena’s latest children’s book, Friends Beyond Measure (HarperKIDS), combines her expertise in illustration and infographics to tell a heartwarming story of friendship. The book follows the journey of two friends, Ana and Harwin, and explores how their bond is tested when a major change in their lives threatens to shake things up.

In this interview, we have a chance to learn more about Lalena’s creative process, her inspirations, and how she brings together seemingly disparate fields to create something truly unique.

Where did the inspiration to write a children’s book through data visualization come from?

Designing for Blue’s Clues, a show for preschoolers, sparked my desire to create my own children’s stories. After a lot of learning and drafting, I had a book ready to pitch to publishers. But that book was not Friends Beyond Measure! It was a lyrical bedtime book. And in the pitching process, someone looked into my background and commented that I might also consider creating a children’s book with infographics. I thought this was an interesting idea.

So I checked out all the children’s books I could find that had information graphics. I saw a number of beautiful nonfiction ones, like Animals By the Numbers, and Professor Astro Cat’s Atomic Adventure. No point in trying to top those! I wanted to do something different—something that would stand out.

I wondered if charts and graphs and maps could be used to tell a children’s story — one with an emotional arc. This seemed like a fun challenge. I had never seen this before. My good friend was moving across the world at the time, and I was quite sad about it; so I tapped into my own feelings for Friends Beyond Measure.

The book’s proposal is an original idea, and we can see it was executed with a lot of care on how much a child between the ages of four and eight would understand. What were the editor’s inputs on how this would be received/perceived by children?

The feedback I got from my HarperCollins team, who were wonderful, had mainly to do with narrative arc and story points. There was one chart they thought would be hard for kids to understand—it was a scatter plot. I could see how it might be a bit confusing to a child, so I replaced it with the set of “parts to a whole” charts—a type that I didn’t previously include, but that is very useful and common. So it was a good change.

A common subject in kids’ literature is to surround, in the most playful way, subjects that are hard for the infant brain to handle—like feelings, body changes, being far from a loved one. As a second issue, your book also tackles data education. In a world that seems to ignore the need for data literacy, how do you see the potential of your book in being the first contact of children with this universe, with this skill? 

I hope Friends Beyond Measure sparks conversations with kids and grown-ups about visual communication as well as emotions. And about how people close to each other can have big differences.
Visual thinking comes naturally to a lot of kids — it did to me (though I was also very verbal and a voracious reader). Dr. Temple Grandin has been bringing a lot of attention to this in recent years, pointing out that we can’t afford to neglect these skills in kids; we must cultivate them. It could even be that some kids will understand the book more intuitively than their grown-ups, who for their part can, while reading the book with their kiddos, ease into more comfort and enjoyment with graphs, maps, and diagrams. And feelings too!

Your background is in journalism, with a Master of Fine Arts degree and a career in music. You were also a contributing graphics editor for The New York Times, designing diagrams and charts for news and for the science section. How did science and data become one of your interests and part of your work?

Artists and scientists have traits in common: curiosity and imagination.

As a small child, when I asked how babies are made and grow, my mom wanted me to know the truth. And growing up, I was surrounded by recurring family illness, so the language of medical science became second nature. My family spent a lot of time at the Gulf of Mexico, and marine biology captured my imagination; the oceans hold so many mind-boggling creatures. And I loved tracking hurricanes, using maps printed on grocery bags during hurricane season.

I was not a kid who liked arithmetic; it was boring and tedious. That’s why it’s important to me to help kids see more of the fun in math. I did love drawing, and I loved organizing things. That’s the connection I make now, with my enjoyment of information graphics. And in middle school, I did a 180-turnaround in math when I started algebra; I had a great teacher, for one thing. And in algebra and the other higher maths, you basically are organizing and simplifying information, which I find almost comforting to do!

Can you describe your experience with handling data to create infographics? What are some of the biggest challenges you’ve faced in this process and how have you overcome them?

In my earlier years at the Times, I often created science graphics, and I enjoyed drawing diagrams of organisms and systems. Appreciation of numeric data did not come as naturally; I learned how to work with it over time and how to have fun transforming it into visuals. And I now relish the opportunity to transform numbers into a visual design.

At the beginning of the pandemic, when I rejoined the Times team, my task was mainly to visualize Covid-19 data for the “Morning Newsletter.” I was examining the immense spreadsheets of cases and deaths from every county in the United States, and every country in the world, every day—it was daunting at first! But I learned from my amazing and patient colleagues how to manage it, and crunch it in all these different ways, and I grew to feel very much at home in those spreadsheets.

In your artist statement, you say that you make art that “forces dualities together, exposing the absurdity of assumptions about strength and dominance.” This contrast seems to be present in many ways in all of your work (a mother-daughter punk rock band, for example). Motherhood and the female family presence is also a strong influence. How do you see these contrasts in the experience of raising a child?

I love this question! In motherhood, I feel I am regularly swinging back and forth between asserting dominance and showing vulnerability. One way to manage that is to laugh at oneself sometimes—like I am in our song and music video for “Because I Said So”! You need your child to obey you unquestioningly sometimes, to keep them safe. But at the same time, in order for them to grow into an adult who can adapt to change and maintain strong relationships, we have to be able to show we can admit when we’re wrong, or even just don’t know what to do.

Do you see data visualization as one of these dualities? Considering that art and logic go hand in hand in this area—two things absurdly assumed as, respectively, female and male.

Some might say data visualization is a counterpoint to more organic, visceral forms of expression. But as with a lot of apparent dichotomies, I think they aren’t oppositional at all. Illustrating data is a form of communication. Does data stand in contrast to, say, feelings? One might characterize data as “facts” or “truth.” But feelings are just as real, and just as truthful. This idea is exactly what I was trying to tap into with Friends Beyond Measure.

If we are making a Venn diagram between art and data (as the main character in your book loves to do) who would be your inspiration in each one of the sections (art, data, and the intersection between them)?

Ha ha! This is great. Okay, let’s see…

The post Lalena Fisher: Telling A Children’s Story With Infographics appeared first on Nightingale.

Well, if you’re a columnist and data guru for The Washington Post like Philip Bump, you tell it through data and visualizations—lots of them, as a matter of fact. Philip’s book The Aftermath: The Last Days of the Baby Boom and the Future of Power in America is loaded with charts and figures that bolster his telling of the rise and denouement of the largest cohort of births since anyone started counting such things. I got the chance to speak with Philip about dataviz, the book, and his weekly newsletter, “How to Read This Chart.” The text of our conversation follows, edited for brevity and clarity.

Kyle Dent (K.D.):  I’m curious about the extent that data itself was an impetus for the book. Was there a dataset that you ran across that got you thinking about your thesis, or was there something else that made you realize you needed to explore generational data?

Philip Bump (P.B.): Not really, no, it was not data-focused initially, except that I have for a long time been interested in the idea of generational differentiation. I first wrote about it when I was back at The Atlantic in 2014. Every couple of years, there’s one of these outbursts of, “What generation am I? What generation are you?” sort of debates, which I leapt into with great eagerness and was surprised to realize that the Census Bureau recognizes the baby boom as the only demographically defined generation. So at that point, I just poked around a little bit. It was fun to have the counterintuitive take that “you don’t need to worry about your generation because it’s all made up.” But then I started to consider this odd and tenuous political and economic moment that we’re in, and how much of it actually overlapped with generational disputes. So that was the trigger for the book, which then obviously led to a lot of data analysis in order to assess the question.

K.D.: How did you decide what kinds of things had to be communicated visually?

P.B.: You’re actually asking a more fundamental question than you realize, which is, how do I ever do that? And the answer is that I just sort of generally think in terms of data and numbers as a latticework on which to hang an argument. It was very natural as I’m exploring this to first consider how many boomers are we talking about? When we talk about wealth by generation, what does that look like? What are the numbers that the Federal Reserve can give us on wealth? How does that overlap with the actual size of the population? When we look at the baby boom, we naturally think about when the baby boomers are going to die, right? When do people die? How has longevity changed over time? There are all these ways to ask questions about what the baby boom looks like, and how it has evolved that gets you into questions of the data. It is very useful then both to understand the data, but also to present it to readers, to provide a depiction of the actual numbers that underlie the generation and its unique characteristics.

K.D.: Sure, there’s lots of data analysis, but a lot of stuff can be conveyed through words. How did you think about, well, this should really be a chart, or I need to graph this because maybe the words aren’t doing the job? Is there any of that in your thought process?

P.B.: Generally, no. I tend to err on the side of, if I have data, then I’ll chart it. It’s interesting, because when we did the audiobook for the book, there was a lot of, okay, well, how do we deal with this? Because they’re not used to having books that have 127 charts in them.

When we did the audiobook for the book, there was a lot of, okay, well, how do we deal with this? Because they’re not used to having books that have 127 charts in them.

K.D.: Wow, an audiobook. I never would have thought about doing that.

P.B.:  Yeah, right. So, that was the point at which I had to think, do we need this, or is the point made in the text? The audiobook ended up using maybe a third to half of the charts. Then I just had to write little descriptors for them. “In the book there’s a chart that says X, Y, and Z,” which hopefully does not break the flow too much for listeners. But yeah, that was the point at which it was like, okay, do I need this chart or not? There were certain charts that I cut out of the [print] book, in part because they weren’t necessarily compelling or conveying what I wanted to say visually, but otherwise, if I had a dataset, I usually had a chart. Then if I had a chart, it usually ended up in the book.

I’m going to invent a phrase here: They say a picture conveys 1,000 words. I just invented that, but I think that is actually a true observation. Instead of saying, well, the baby boom started in 1946, and rose to this, and what if… just show a graph of it. That was sort of my approach.

K.D.: What about balancing the complexity of those charts? You must have to keep a very clear picture in your head of who your audience is.

P.B.: Yeah, absolutely. The book is not meant to be an academic work, right? I publish a newsletter, “How to Read this Chart,” which I started after I had already done the book deal, and the book editor said, “Hey, if we’re gonna do a bunch of charts here, it would be really helpful if there’s just always a little blurb that says how to read this chart.” And I thought, you know what, that actually would be generally helpful. So every chart in the book, regardless of how simple it is, has a box underneath that says “How to Read This Chart” that also includes sourcing information with footnotes and so on and so forth. But my goal was to make charts that were interesting and then do my best to explain them. The New York Times review of the book suggested that some of the charts were hard for them to read. Totally fair, the charts are not everyone’s ballgame. But I thought it was true to myself to include the charts in the way that I did, and I think that for most casual readers, if they sit with the charts for a second, they’ll get the hang of them because I did my best to try to explain them.

K.D.: And, of course, a reader could decide, I don’t need to understand this chart completely, but I still get the idea.

P.B.: Hell, if they want to email me and ask, I’m happy to talk about it. The genesis of the newsletter—in addition to having an audience of people who might be interested in this stuff for when the book came out and pointing to my work at the Post—really was to have people just feel comfortable with data visualizations. The newsletter is not really data “visualization-y” as such. It’s not about, here’s how you do a scatterplot and mark your axes, right? But it is presenting data in a visual way, in an accessible way, in order to have people just feel like, normally I don’t pay attention to these sorts of things, but now I feel more comfortable with it because I’ve been getting this newsletter. That’s the ideal anyway.

K.D.: I’m a regular reader, by the way, of your newsletter. I think it hits that right point. It’s not instructional, but it gets people to think about, if I have this data, how can I depict it in a way that gets the idea across.

P.B.: Most of the readers aren’t data visualization geeks. Perhaps, in part, because it’s like getting a How to Drive a Car for Dummies book. Who wants to buy that book and carry it around the bookstore? If you’re a career data visualization person your first reaction is probably, I feel like I know how to read a chart. But there are times I get into the actual mechanics of making a chart just because people have asked, and so I try to answer that. It’s sort of all over the board in terms of skill level.

K.D.: Regarding the “How to Read This Chart” explanations in the book. Did writing any of those make you realize, oh, this chart’s too complicated? I have to rethink this chart completely. Or more generally, did having to write a description affect your design at all?

P.B.: Probably. I can’t think of a specific example where that happened. In part because the way I normally work is that I’ll make the chart and then write around it. So, the chart making is generally done before the actual argument I’m trying to present or story I’m trying to tell.

I’ll make the chart and then write around it. So, the chart making is generally done before the actual argument I’m trying to present or story I’m trying to tell.

There were a series of charts, actually, in the first chapter, that I really struggled to make easy to deal with. My wife is my constant guinea pig for stuff like this. She is much better at reading charts than she pretends, just as a side effect of having been married to me for an extended period of time. I can put a chart in front of her and ask, does this make sense to you? And if she’s not like, yeah, this makes sense to me, then I know I have work to do. There are some charts that I will admit are probably more complicated than they could have been. But, we do our best.

K.D.: As a book author, I’m guessing you had a lot more freedom to include charts than you do as a journalist. With normal article constraints, you probably have to limit yourself to just one or two.

P.B.: Honestly, at the Post, God bless The Washington Post, limiting the number of charts in an article has never been something that has been imposed on me. I’ve literally had 50 charts in an article before.

K.D.: No kidding.

P.B.: I mean, this isn’t for print. I write mostly for the web. When I had an online excerpt of the book, I included several charts in the excerpt, but when it actually ran in print, they excluded them. The web gives me the freedom that print does not.

K.D.: I see. I was imagining the book would be a liberating experience, that you could go hog wild.

P.B.: No, no, happily, I’m hog wild in all facets of my employment.

K.D.: Lucky you, that’s great. But what’s the experience like of writing a book as opposed to an article?

P.B.: It’s hard; it’s hard. There were times when it was definitely a drag. I’m used to writing a lot. I write several 1,000-word articles a day. Writing prolifically has never been a challenge for me. But, this is carrying a cohesive argument over the course of 120,000 words, and bracketing that into several 20,000-word chapters. It’s just hard to do that. It was an interesting challenge, and I am not eager to start another lengthy book project right now—give it another month or two. But the end product was worth it.

K.D.: In a book I imagine you’re thinking about how to build up your thesis. An article has some structure, but it doesn’t necessarily have the same need to introduce concepts that you can build on later that a book requires.

P.B.: Yeah, if you think about it hierarchically, a normal article will be a couple of tiers, the broad arguments and sub-points. This was much, much more densely layered. The book has an overarching thesis; it’s got two sections, the baby boom, and then the aftermath. It’s got sub-sections and chapters in each of those. I don’t need to tell people how a book works, but when you are actually trying to construct it, it’s much, much more intricate than it probably seems to an outsider who’s never written a book before.

CHARTING METHODS TO SHOW CULTURAL AND TECHNOLOGICAL TRENDS

K.D.: You talked to a lot of people for the book, from demographers to experts in U.S. cemeteries. Sometimes you’re getting simple facts from them or sometimes anecdotes. And other times you are working with large datasets, and all of this has to be woven into your narrative. What’s the approach to synthesizing data of various kinds and various scales?

P.B.: This was really the primary learning curve because I’ve written a lot of articles in which people are quoted or that include datasets, but again, the hierarchy is shallower and you’re slotting this into 1,000 words. A chart is either going to come at the 400-word mark or at the 800-word mark. When you’re doing that at 20,000 words, you really have to figure out a structure. I tend to write start to finish, so this book was written almost entirely from page one to the end in order. That’s how I think. So that’s how I did the chapters. Beforehand, I took all the quotes that I thought were good quotes that spoke to the subject matter at hand. Then I organized the quotes from people and those were often overlapping with datasets that I had or data that I already pulled up. I structured it where I had the quotes that were talking to the points I wanted to make, and a general sense of where the graphs would fit in. And then I connected it all with either narrative elements or normal transitions in the way you do in writing.

Because I do so much more with charts, I felt much more comfortable integrating those into the text. I’ve interviewed a lot of people but, you know, most of my stories are data-based and not quote-based. I spoke with over 100 people, and there’s people who I spoke with who were very gracious with their time but their quotes never actually got included in the book, which I feel bad about, but you know, you gotta cut something.

K.D.: In the book, you talk about some data that was inaccessible. I’m thinking of Billboard’s trove of data that hasn’t been digitized. But that was solved because of crowd ingenuity and because people seem to care about data. Was there any other data you had a particularly difficult time accessing or otherwise using?

P.B.: That’s a good question. I’ve been doing this a long time now, and it’s taken me a long time to realize I’ve been doing it a long time. But, I’ve been finding weird stuff online for decades. And that really served me well here because it allowed me to evaluate data sources. The data to which you refer about Billboard’s top rankings of music, I knew that would be useful in the chapter that assesses the cultural legacy of the baby boom, but I also knew that dataset is crowd-sourced, an ad hoc digitization of the Billboard charts. I knew that existed because [blogger] Andy Baio who has this great site waxy.org had written about it back in 2008. I’m an old internet hand, so I know where a lot of this stuff is stashed around the web. I can’t think of anything where I was constrained in that way. There have been a couple of occasions where I’ve since thought of things that I would have liked to have included, and I’m sort of kicking myself. But yeah, that’s how it goes.

I’ve been finding weird stuff online for decades. And that really served me well here because it allowed me to evaluate data sources.

K.D.: Another question I had for you was about other really cool datasets you found for the book, but I guess, since you’ve got this deep well of data sources…

P.B.: The coolest dataset I found was not data in the sense of numbers. I have a friend Matt Novak who writes Paleofuture [a blog about past predictions of the future] who does a lot of time capsule stuff, which got me thinking, okay, when was there a cool time capsule that I could write about? I poked around newspapers.com, the archive of newspapers, which was absolutely invaluable for this. It is such a great resource. I stumbled on information about a time capsule that was done in Birmingham in 1950 when they built a new city hall. The time capsule included letters from civic leaders and things along those lines, written to people in 2050. I went back and read news reports about what those leaders were saying to try to get a sense of how accurate their predictions were. One letter that I came across—and I actually got a copy from the Birmingham Public Library—was from the city’s police commissioner talking about what policing was going to be like 100 years in the future. That police commissioner turned out to be Bull Connor, who within 20 years was absolutely notorious for being a horrible racist who was beating Black people as they went to register to vote.

The fact that I had this letter, and I don’t know that it’s been seen since 1950, really struck me. That he had intended for people in 2050 to go back and look at the way in which he viewed the future knowing what his immediate future was, was very humbling when I’m thinking about trying to make any predictions about the future. But it was also just this fascinating piece of American history. Just a very, very small piece. Certainly nothing world-beating in terms of the historical revelation, but it was just a fascinating encapsulation of our often limited ability to understand what lies immediately in front of us.

K.D.: That’s amazing that you happened to access this artifact that is so relevant to the civil rights aspects of your story. It’s incredible to me that it synced so well with the book itself. 

P.B.: Yeah, it was fortuitous. But there’s a lot of stuff like that. There’s certainly an element of luck to it, but I’ve been at the Post now for nine years and was writing for the web before that. Recognizing that serendipity when it strikes requires being familiar with it to some extent, too. So, when I saw that, and I knew that the chapter also included a look at how much the United States was wobbling in terms of its embrace of pluralistic democracy, and how that obviously reflects back to what was going on in the South in 1950. It all just sort of snapped into place.

CHARTING METHODS TO SHOW DEMOGRAPHICS AND VOTING TRENDS

K.D.: Back to the Billboard data, you used boomers’ musical taste as a proxy for cultural influence. In general, how do you judge if some particular data you have accurately represents the feature that you’re analyzing? Is there a risk that you might be looking for your lost keys where the light is good?

P.B.: That’s absolutely a risk, but I think that as long as you are honest and direct about what you’re doing and what you’re working with, I feel like it’s okay. I acknowledged in the book that this is an attempt to capture this particular element. What results from that data is a chart in the book, which basically shows that the artists who were popular when baby boomers were teenagers have essentially dropped out of the Billboard charts. One would expect that, but it is still a reflection of the decline of cultural influence of the baby boomers. I presented it as one aspect of that cultural decline and not necessarily saying this is a marker that the influence is doomed. There’s also a caveat in there about the Beatles who had this thing on Disney+, which brought the Beatles back into the Billboard charts in 2022.

K.D.: We talked about the audiobook a little bit, and how you had to alter things for that. But even in the printed book, you couldn’t use color, for example. Did you have to think about the visualizations differently knowing you would only have grayscale to differentiate?

P.B.: I used to work at Adobe as a designer, and that is useful to me both in my day job and for the book. I tend to make my charts in Adobe Illustrator because I’m used to it, and I’ve used Illustrator forever. I just made a design template that had swatches of grays and a scale and had header text and so on. Obviously for the Post I have design standards and a design template that’s mandated by the Post’s brand guidelines. So I basically just tried to do that for myself for the book. One of the first questions I had for the book editors was what typeface do you want me to use for the charts because I wanted to make my own charts. So that just got put into the template. When I first got prints of the book, I had to tweak some of the grays, but because I was using a template, that was trivial.

K.D.: That gets into my last question, which is one everybody’s always curious about. What are your go-to tools for performing analysis and visualization?

P.B.: I have never taken a design class in my life, so I have a very weird, ad hoc, kludgy sort of system. I use Excel a lot. When I’m parsing big data sets, I use Perl, which is so archaic it’s ridiculous. That’s the first programming language I learned besides BASIC when I was a kid. So now I’m pretty adept at Perl and in parsing big data sets. I also use this site called RAWgraphs.io, which uses a Javascript library to create charts, and it’s very easy to drag and drop columns of numbers and generate more complicated graphics. I used that both for the book and at the Post. I’m not an R guy, not a stats guy; I’m old school. But I don’t claim to be a statistician, so I don’t need this statistical toolset that others might. But, yeah, it’s clunky. I use Illustrator all the time because it makes nice crisp print graphics.

K.D.: So, you load data into Illustrator’s little data dialog box.

P.B.: Oh, yeah. It’s a drag; it’s stupid. I’m sure there’s a better way, but I’m old. I’m older than I seem. We get set in our ways, and I used to walk to school uphill both ways and so on.

K.D.: Yeah, it was really rough “in those days.” I used to do a lot of work in Perl myself. It’s been ages, but if I’m not mistaken, you don’t have the kind of support like you get with Pandas and Python for managing datasets, right? So you’re really using Perl just to get data formatted into what, rows and columns, I guess?

P.B.: Yeah, exactly. When I have a massive dataset, as with COVID data when I was doing a lot of COVID stuff, you’d have these daily datasets by county over the course of two years’ time. If you need to pick out what data figures were at the start of the month, it’s very easy to do that in Perl. I have Perl scripts I can just pull up as needed. Kids, if you’re listening at home, you listen to teachers and do it the more elegant way. This is not elegant at all.

The post Philip Bump: Telling the Story of the Baby Boomers in Charts appeared first on Nightingale.

1. What’s one topic you would love to visualize but have never had the chance to?

My immigration adventure. Over the past two years, I haven’t lived in the same country and city for more than six months. Because my Green Card process for U.S. permanent residency was delayed due to Covid-19 (the government shut down for a few months and my paperwork took longer to process), and because I then decided to change career paths and had to apply for a German Blue Card residence permit, I ended up moving a lot and living in the United States, Curaçao (a beautiful Dutch island in the Caribbean), Argentina, and Germany over the course of those two years. I worked remotely while visiting friends and family, made new friends, learned a new language (Dutch), and got to know myself better. This is something that I have been wanting to visualize for a while now, although I haven’t had enough time to brainstorm and think about what exactly I would like to visualize and how. 

2. If you had to choose an entirely new career path, realistic or not, what would it be?

When I was 17 years old and had to pick a college major, I made a list of all the potential career paths that interested me. Audio/sound engineering, petroleum engineering, violoncello, philosophy, and international affairs were on that list. I ended up getting a degree in economics and later on in data science. Although I very much enjoyed what I studied, I would change careers to aerospace engineer because I am a big fan of Elon Musk and would love to travel to space one day.

3. If you could be any type of chart, what would you be?

I would be a sparkline. I like sparklines because they are versatile tiny charts and minimalistic. They can be accompanied by numbers, words, high and low values, average lines, etc., or just be presented on their own to show the overall evolution of some variable. In Beautiful Evidence, Tufte described sparklines as “a small intense, simple, word-sized graphic with typographic resolution.” You have probably seen sparklines on your Twitter account stats or your Stocks app on your iPhone. If you have, you may have noticed how they easily fit into so many different contexts without compromising the data-ink ratio. This is why I like sparklines.

The post Three Questions with… Guillermina Sutter Schneider appeared first on Nightingale.

Today, we’re introducing you to our newest team member, columnist Will Careri. He’s written for us before, and we can’t wait for you to see the new series he’s working on now!

Throughout his career, Will Careri has been a trusted advisor and practitioner of public relations, marketing, and design for brands all over the world. He’s the newest addition to the Nightingale core team and will be graduating from the Maryland Institute College of Art in December 2023 with his master’s in Data Analytics and Visualization.

1. What is one visualization that has inspired you and why?

It’s hard to think of those who inspire me without thinking of Federica Fragapane. Her visualization “Space Junk” is one which stands out to me among her many great works. For me, while I know the future of data visualization is going in this direction of interactivity, it’s just as exciting, if not more, to sit down and just stare at a static visualization for as long as you can. The more you look, the more you learn. Her work exemplifies the notion that data visualization is an art form. As someone who entered the data visualization field from the design avenue, projects like “Space Junk” and “Sky Map” will always be a source of inspiration.

2. What’s one topic you would love to visualize but have never had the chance to?

I actually did have the chance to visualize the topic I’m thinking about, but wish I had more time to go back and revisit. Visualizing the Tommy Westphall Universe was a project I took on in early 2022. It took me roughly three months to finish, but I still think I can do better. The Tommy Westphall Universe is a fan theory based on the 80s drama “St. Elsewhere,” in which it’s theorized based on the show’s finale, that the entire plot of the show took place in the mind of a young boy named Tommy. Following this theory, it would mean every show connected to St. Elsewhere takes place in the same universe, all within Tommy’s mind. It has been decades since the theory was first proposed, but since, over 500 shows have been added to the Tommy Westphall Universe. Connecting everything from The Andy Griffith Show to Doctor Who to NCIS to Brooklyn 99. It’s a hefty project to visualize, and one I’m sure will stump a handful of data visualization artists who choose to take on the challenge.

3. If you had to choose an entirely new career path, realistic or not, what would it be?

Owning my own used bookstore. Anyone who knows me, even for a short while, knows how much I read and the problematic number of books I own. I love the idea of creating a space in my neighborhood for people to not only find books they love, but giving used books a new home.

A close second choice would be a national park fire lookout; although I think that counts more of a seasonal job than a career, and the jobs themselves are becoming more and more scarce.

The post Three Questions with… Will Careri appeared first on Nightingale.

Emily Barone is a career journalist who, over the course of about two decades, managed to go from hating math class to running newsroom data literacy trainings. She’s worked at several national publications—including most recently at TIME magazine, where she wrote and visualized data-informed stories about health and climate. She is slightly obsessed with the challenge of translating large graphics into mobile-friendly versions. Her favorite tools of the trade are Python, Datawrapper, and colored pencils. During off hours, you can find Emily attending French classes, strolling around public parks, and rock climbing. 

1. If you could be any type of chart, what would you be?

A scatter plot, because they show relationships. Relationships, both in data and in life, are so important! Also, in just the last few months, I bought a house, moved to Connecticut from New York City, grew my household from four people to five, adopted two cats, and landed a new job at the wonderful Data Visualization Society. So much change at once has certainly left me scatterbrained at times.

2. What’s one topic you would love to visualize but have never had the chance to?

An autobiographical timeline of my adult years, told through my Amazon cart. My order history goes back to the early 2000s, when I bought The Healthy College Cookbook: Quick, Cheap, Easy ($13). That already tells a lot about what was going on in my life! Then there are, of course, the milestone purchases for the first apartment, the wedding, and the waves of pregnancy and baby gear—more like a tsunami for the first kid. But the odds and ends that I bought along the way also tell a story about my hobbies and intellectual interests, my travel and vacations, and even my career. Some even flick at world events, like my pandemic-era home office chair splurge ($300). It’ll take a long time to categorize all the orders in a way that would make sense for a visualization, but I do hope to start the project soon, as it will be a work in progress until, well, you know.

3. If you had to choose an entirely new career path, realistic or not, what would it be?

I might choose a path that would let me work with plants. I love gardens, parks, and arboretums—they are truly my happy places! But it’s hard to say whether this path would be realistic. Despite my deep affection for greenery in nature, I have yet to own a house plant that I haven’t murdered.

The post Three Questions With… Emily Barone appeared first on Nightingale.

The practice of creativity has been on my mind more than usual lately (perhaps because of Alli Torban’s excellent creative mini-series). How do dataviz makers foster creative energy while maintaining a sustainable relationship with their work?  For early career people especially, does our professional practice of dataviz affect our personal style and default ways of thinking? Hoping to dig more into creativity, I decided to consult Ana Cuza, a friend I made through the DVS Early Career Committee. Ana is from Romania and works as a data visualization analyst at Northwestern Mutual. Like our previous conversations over chai in Brooklyn, this conversation left me in a swirl of thoughts and with even more questions (in the best way).

An excerpt from this interview appeared in Nightingale Magazine Issue 2.

Simran: How did you find your way into data visualization?

Ana: Like many people in this field, I was interested in multiple things: architecture, design, math, and so on. In college, I decided to study computer science and did research in artificial intelligence and machine learning. The more projects I did, the more I became interested in AI fairness, bias, and the way current data science practices affect our society. On the other hand, I loved visual arts and creative writing. I was drawn to the field because of how new it is and the opportunity to forge my own career path.

Simran: What are your favorite sources of inspiration?

Ana: I love books! I always enjoy getting new ones. I’ve also been trying to find inspiration outside of the dataviz world. Before, I used to read The Pudding a lot and followed a lot of dataviz people on Twitter. Now, I started going to a design group in New York and attending design lectures. I try to find inspiration from daily life. I think we sometimes end up in a bubble as a field, throwing around the same ideas.

Simran: Are there any particular books that you’d recommend to other dataviz practitioners or books that have stuck with you?

Ana: Data Sketches by Shirley Wu and Nadieh Bremer. I don’t make a many maps, but I feel inspired by the work in Atlas of the Invisible. A book that I read initially and still re-read is The Functional Art by Alberto Cairo. As someone working in the business world, I have been reading Everyday Business Storytelling. Otherwise, I love going into bookstores and flipping through the books in the design section.

Simran: How do you define creativity, given your role in the business world? We often think that creativity and business aren’t compatible, but how do you think about it and practice it? 

Ana: I try to think of myself as a full person who is not defined by my job. It can be hard to let go of the tunnel-vision thinking of, “These are the things that will make me successful in the future.” That’s really limiting for who you are as a human being and your career. I think of creativity as this side of my brain that is messy and not coherent yet, a playground. I do work where there are a lot of opportunities for growth and learning. At the same time, I also constantly challenge myself to get out of the bubble of my work and figure out other things that fulfill me with the hope that I’ll move towards work that feels more fulfilling… What is fulfillment? I guess, work that brings some value to the world and brings me joy in my everyday life.

Simran: How do you find the time and creative energy for your personal projects?

Ana: Honestly, it’s only when I have less going on with work that I have the energy to work on my own projects. I tried to collaborate with a group to work on a couple of projects and write about dataviz, but we ended up taking a step back from that. I also tried working with a friend to start our own project, but I got overwhelmed and put a pause on it. I try to be nice to myself and not get tired of this cycle of starting and stopping things. At the end of the day, it’s about doing work that makes you feel good.

Simran: After graduating amidst a pandemic, what are some of the things that have surprised you since starting a full-time role in dataviz? Has that changed your relationship with this craft?

Ana: I used to go into projects that sounded interesting and give everything to them, thinking that I’d eventually find my field, my thing. I realized it’s more helpful to focus on what makes me happy on a day-to-day basis. I always loved the idea of design, yet right now I’m working on more data science-related work and enjoying it, challenging the idea I had of myself. The discussions I’ve had with others in dataviz make me realize how new the field is and how there aren’t well-established career paths. The responsibility of making sure you are growing in this field, that you are getting paid enough, that you are finding exciting and meaningful work falls on you. When you advocate for yourself, you end up advocating for those coming into this field by building more opportunities for them.  

Simran: Could you expand more on what it means to advocate for yourself, especially in this field?

Ana: I’m someone who has difficulty trusting my own performance and external validation. I’ve been lucky in the past two years to have a stream of people coming into my life supporting my interest in dataviz, personal life, and work. They support me even when I’m failing or doing things outside the traditional definitions of success. The community you build around yourself is really important for both your career growth and how you think of yourself and your potential.

Also, always negotiate your salary! Even when we read that advice over and over again, it can feel difficult to actually negotiate due to the context and thinking the organization did their best, but please just do it!

Simran: Do you have any advice for those who are trying to land their first opportunity in this field?

Ana: Listen to yourself first! Make note of what originally brought you to this field. It can be easy to get distracted by communal notions of success, so going back to those original ideas can help you do new things that you find personally fulfilling, even if they look messy to the outside world. Embrace messy; it’s fun.

Simran: You mentioned uncertainty in yourself… How does that uncertainty affect how you develop your taste, style, and voice? For instance, when you are trying to decide where to put an annotation and each option looks equally good to your brain, what do you do? 

Ana: While I ask my coworkers for input on work and personal projects, what has been helping me the most is embracing imperfection. In a year, I’ll be looking at work I did and cringing, wondering what I was doing. I think a lot of us get stuck in that place of thinking we can do better and spend time trying to get to a state that matches the vision in our mind. The place you actually find your style is in your own imperfections and most cringeworthy work because you go back to it and reflect on what drew you to a particular element.

Simran: I know that you helped build your organization’s dataviz style guide. What has the experience been like creating an organization’s style guide and being on such a young team? Have you ever felt imposter syndrome about working on this task?

Ana: Yes. I just read certain books a year ago and now I’m making these decisions, constantly learning new things. My coworker and I come from theoretical backgrounds. We also have people within the organization who have this great intuition for building and communicating dataviz without necessarily having a background in data visualization. It’s interesting how much we have inspired each other. Thus, the style guide is a reflection of both the culture of presenting dataviz within the organization and the expertise we bring. Our style guide is a living document. For example, when building the color palette, we used online tools to make sure the colors were accessible and that everyone could use these colors. Six months later, someone told us that they were having trouble with the colors, so we went back and updated everything. It’s a reminder that even when you think you have the theory down, it’s always important to root the guide in people’s actual experience.

Simran: Is there anything we haven’t talked about today that you think is important to discuss with early career folx in our field?

Ana: I’ve been thinking about how, as a generation, we live in a world where sad things happen a lot, things that can be difficult to process. How do you grow yourself as a person and focus on your passions and career and be connected to what’s happening in the world without compartmentalization? I think it’s so important for us to build up the world the way we want to see it, even if that means being rebellious and ignoring past ideas of success–there is nothing clear about our future. How do we do that? I think it comes back, as always, to community.

Thanks for tuning in to our Early Career Corner series! Be sure to check out this page for more ways to get involved with the early career community at DVS!

The post <strong>Early Career Corner: Meet Ana Cuza</strong> appeared first on Nightingale.