It’s the size of the smile on your face when you see someone. It’s the decision to stay in touch with a niece long after family events end. It’s the fragile silence between you and a friend who couldn’t support a recent life choice.

As a data designer, I’ve always been obsessed with how we categorise the intangible. Recently, I set out to map the people who have shaped me. I didn’t want a balance sheet, but I did want to see the patterns. A relationship always evolves; this would only represent a snapshot in time.

The Taxonomy of Connection

I began by listing every person I care about. First from memory, then verified by my friends list on Facebook. But as I opened my spreadsheet, the questions started to flood in. Can family members count as friends? For example, my nieces and I have been chatting nonstop for years now. We grew fond of each other through the circumstance of birth, but we stayed in touch by choice. Does that make them friends? And what about friends who aren’t supportive of my life choices? We were very close 7-8 months ago, but we are not now. Are we still friends? If I exclude her from this, does that mean I have given up on our friendship? Also, I use the term “friend” very loosely. I am naturally familiar with strangers. Is my new neighbour — with whom I have shared a few cups of tea — my friend?

To make sense of the friend list, I distilled friendship into three core metrics, scored on a scale of one to three, three being the highest rank possible: 

• Reliability: Loyalty, faithfulness, and the feeling of being safe.
• Empathy: Supportiveness, kindness, and open communication.
• Joy: Playfulness, liveliness, and shared common ground (though one might question whether friendship is required for common ground; for the sake of this visualisation, I decided it was).

I also added two judgment values: Duration (how long we have been friends), and Contact (how recently we spoke). To keep the data honest, I limited the scope to friends I had contact with in the last 24 months. I chose 24 months as a mark because it’s the period since my daughter was born. Spoiler alert: In a time when I often felt lonely as a new mother, the data showed me I was actually deeply loved.

From Sketching to Scripting

In my notebook, the design evolved rather quickly into a series of “tiles.” I remember having the visual in my head for a while, and I felt as if I were a vessel letting it out onto the paper. I wanted something that would represent the scale’s levels easily. Level one was a simple base; level three added complex detail. 

Initially, I used background colors to denote duration, but the palette was too loud. It made the story about “how good I am at making friends” rather than “how these friendships built me.”

Then came the pivot. Usually, I build these visualizations by clicking the mouse. A thorough process of copying, pasting, and double-checking layers in Illustrator and Figma would easily take three hours. But, inspired by the “vision to execution with a click” movement, I turned to Claude and Gemini.

I asked Gemini to help me write the prompt for Claude. It generated a Python script that processed my Excel file and generated stacked layers as PNG files. Claude taught me how to install Python on my Mac. (Honestly, I felt like I was back in the 90s, typing into a terminal to launch a game.) Then, “Boom. Your tiles are ready.” With a single click, the assets were generated. A few back-and-forths with Claude, and the grid was aligned. The work was done.

The Cost of Efficiency

As I looked at the finished folder, a strange feeling washed over me: I didn’t recognize the data. By automating the execution, I had accidentally bypassed the data familiarization stage — that meditative hour where you handle each data point with care and remember the person behind it. The tiles were beautiful, but they felt distant.

It raised a fundamental question for our field:
If the AI builds the layers, are we co-creators? Or are we just curators of our own memories?

The Tokens of Gratitude

Despite the digital distance, the final grid is a testament to my life. These tiles are me. They represent the people who stayed through puberty, the ones who signed my wedding book, and the new friendship that started when I collected my son from preschool, which grew close.

This project is more than a visualization; it’s a token of gratitude. It captures a snapshot of my soul as it exists in 2026. Shaped by humans, rendered by machines, and held together by the voluntary, personal relationships that make life worth mapping.

The post The Tiles That Made Me: Mapping Friendship through the Lens of AI appeared first on Nightingale.

From what I’ve experienced and witnessed working in analytics, I don’t think I see the same strides in how analytics work gets done. For example, how many of us have kept producing while our lives were going through upheaval? How many have wondered if we can stay in our jobs, or even careers, because the way we’re expected to work is unsustainable to our well-being and personal lives? What might happen if we approach our work in a way that decenters speed, volume, and heroics, and recenters all humans involved?

My early days

I discovered data visualization in undergrad while studying cases like the Three Mile Island nuclear accident, where poor information design contributed to near or actual harm. It was one of the first moments in engineering where my ears perked up, especially around how data visualization bridges the analytical, creative, and human.

My early roles in quality improvement in hospitals only deepened that passion. I was fortunate to work alongside clinicians, designers, and researchers who introduced me to co-design methods, the importance of evaluation, and reframed users as collaborators.

Eventually, I landed my first role on an analytics team to support with BI design and development. However, it was during a time when my mom was battling appendix cancer, and I was living at home to support with caregiving. And my passion for this work quickly collided with the realities of how analytics gets done.

Deadlines versus trauma

When my mom was admitted to palliative care a year later, it happened to line up closely with a due date for a “high-stakes” report I was responsible for developing in Tableau, which I was learning how to use on my own. Because of the project’s size and weight, and the responsibility I felt to deliver, I would work a full day, bring my laptop to hospice care, and continue working near her bedside.

I could have asked for an extension or support. However, analytics routinely feels like a pressure cooker, especially on “high-stakes” projects. Plus, my qualifications were openly being questioned by others, I was identified as one of the “single points of failure”, and was also cautioned about the potential for blame if anything went wrong. Stepping away didn’t truly feel like an option – it was easy to feel cornered. On top of that, I was in my twenties, with undiagnosed neurodiversity, zero concept of needs and boundaries, and overwhelmed, confused, and exhausted.

At my mom’s funeral, a colleague asked when I might return to work, and relayed that people were getting anxious about report delivery. 

Her funeral was on a Friday. I went back to work on Monday. I finished developing and testing the report—and from what I remember, everyone received it when expected. 

I’m not sure if it felt like “a win” for me. It made me question, how are analytics workers perceived? And, what did I just do? 

Breaking points

The elements of that experience were not isolated to any individuals, teams, or organization, but recurring threads I’ve encountered and witnessed time and time again as my career in analytics has progressed. 

Fast-forward many years later to a more recent contract, again as a BI designer and developer, where layers of challenging, but common, systemic pressures rattled my nervous system. I eventually had a major Autistic shutdown (an involuntary neurological response to sensory overload), and needed to leave.

I’ve listed some of the challenges below – do any of these resonate, neurodiverse or not?

Structural

• Unclear or missing roles, scoping, processes, and standards
• Unrealistic expectations around task complexity and timelines
• Unpredictability requiring frequent context switching and quick adaptation to change

Cultural/interpersonal

• Persistent state of urgency, with hustle and “just get it done” culture
• Lack of autonomy and space, with ongoing progress checks and pressure points
• Repeatedly having to overexplain, raise concerns, and justify boundaries 
• Interdepartmental conflict and tension
• Feeling held responsible for the success of the project

Environmental

For this experience, I was able to be fully remote. From research and my own previous jobs, I know several factors that can be challenging with in-office environments for Autistic workers. These can include adherence to a 9 – 5 schedule, open concept office spaces with bright lighting and noise, and pressure to attend social functions. 

When layers like these start to compound, my nervous system gets flooded with input and demands, and can’t catch up. I get stuck in survival mode, and eventually break or shut down. Autistic burnout can look very different from our typical understanding of burnout, and recovery can require weeks to months (or even years) of deliberate care. Just to note, other Autistic people may have different experiences, supportive conditions, and responses – these are just my own. aces with bright lighting and excessive noise, constant interruptions, and pressure to attend social functions.

At this point, I’m afraid of returning to analytics as it currently exists. It can feel inaccessible to neurodivergence, and unforgiving to responsibilities outside of work. But am I the only one who feels this way? 

Ripple effects: Tired teams, leaders, products, and users

From what I’m seeing across industry research, I don’t think I’m the only one finding this field challenging and unsustainable. Here are some highlights:

Data teams are already overcapacity, despite ever-growing demands

In a 2023 survey of more than 900 data team practitioners and leaders across the United States and the United Kingdom, 84% said their workload exceeded their capacity, and 90% reported that it had increased from the year prior.

The vast majority of data engineering teams feel burnt out 
Another survey of over 600 data engineers and managers found that nearly all of them (97%) reported feeling burnt out, primarily due to time spent fixing errors, maintaining data pipelines, and constantly playing catch-up with stakeholder requests. Nearly 90% reported frequent work-life disruptions. 70% said they were likely to leave their current company within a year, and almost 80% were considering leaving the field altogether.

“When a deliverable is met, data engineers are considered heroes. However, “heroism” is a trap. Heroes give up work-life balance. Yesterday’s heroes are quickly forgotten when there is a new deliverable to meet.”

— 2021 Data Engineering Survey: Burned-out Data Engineers Call for DataOps

Analytics products aren’t sufficiently supporting our end users

In a 2025 survey of more than 200 product leaders, data teams, and executives, 40% said their data doesn’t support decision-making sufficiently, 51% can’t meaningfully interact with the data provided, and 29% export data to spreadsheets daily. 

Findings I’m not surprised to see, considering how we’re expected to work. From a design perspective, it can be a struggle to carve time and space to sufficiently understand the data and users before I’m asked to quickly turnaround a prototype. Plus, post-launch follow-up and evaluations don’t seem to gain traction before we’re onto the next priority.

We’re hoping AI will save us

In the same survey as above, 75% believe AI-powered analytics might finally help uncover value buried in data. But in a new study by MIT and Snowflake, 77% of data engineering teams are finding their workloads even heavier, despite AI integration. 

While AI has the potential to streamline tasks and improve product quality, a cracked foundation could limit its impact, and cause further complexity and burnout. 

Diverse does not equal inclusive

In analytics, we often point to diversity as evidence that we’re on the right path. When concerns are raised about how pressures, workloads, and expectations may weigh differently across identities, they can be dismissed with the reassurance that our workplaces are “already pretty diverse.”

That might be partially true in terms of representation. A recent study by Statistics Canada showed that 60% of data scientists (one of many roles within analytics) are immigrants, with the majority of first languages being neither English nor French. About one-third of data scientists identify as women+ (defined by the study to include “women and some non-binary people”). 

It is important to recognize that diversity does not always equal inclusion. In other pieces published by Nightengale, Catherine D’Ignazio and Lauren F. Klein, authors of Data Feminism, speak to how racism and sexism are imbued in the end to end data lifecycle, reinforced by structures of power, and ultimately surfacing in our products. An online poll by Christian Osborne showed that 90% of respondents said that they’ve experienced microaggressions at work, which can cause emotional and psychological harm, decrease job satisfaction, and increase turnover. 

We can also be sensitive to trends across all workplaces. In 2024, the Diversity Institute, Future Skills Centre, and Environics Institute for Survey Research published a Canada-wide study on gender, diversity, and discrimination at work. The survey reinforces that workplace discrimination is more likely to be experienced by racialized and Indigenous peoples, women, persons with disabilities, 2SLGBTQ+ individuals, and young adults. It is crucial to recognize that intersectionality amplifies these effects, with racialized and Indigenous people more likely to face multiple forms of discrimination, especially related to gender, age, and disability. And, those who reported experiencing discrimination also reported poorer mental health. 

Even with diversity, we still need to ensure that our analytics workplaces make everyone feel safe, healthy, empowered, and valued. Diversity, equity, and inclusion (DEI) programming remains urgent and necessary, and should not be deprioritized or defunded. In the systemic pressures previously discussed, I wonder how these are felt across different identities. For example – what are the experiences of a woman in a leadership role, a recent immigrant who is supporting family both at home and overseas, or a new grad with one or more disabilities – are they really all the same?

What if we worked differently, and prioritized people first?

The tendency for analytics workplaces to be top-down, reactive, chaotic, transactional, and overburdening clearly isn’t working—not for our people, and not for our products. We’ve got more than enough burned out workers and leaders, and more than enough underused products to prove it. And I’m only seeing signs that analytics (and tech more broadly) might be becoming even more unsustainable—from 996 culture, mandatory RTO policies, pressure to upskill for AI, low data readiness for AI, to the defunding of DEI.

I think systemic change (or a reset button) is required to humanize our approach to analytics work. The shift has to include not only analytics teams, but also the ecosystems that rely on us. 

For example, earlier this year, the Canadian Occupational Health and Safety Magazine suggested that workplaces adopt a trauma informed care (TIC) approach to work. This approach places safety, trust, and empowerment at the center, and recognizes that many of us have experienced trauma—trauma that workplaces can trigger, perpetuate, or even create. Normalized approaches to analytics work can actually be quite harmful, like unpredictability, constant urgency, ambiguity, and the erosion of autonomy. 

The article references the six pillars of TIC laid out by the Substance Abuse and Mental Health Services Administration (SAMHSA), and cites research that shows its positive impacts to employee well-being, satisfaction, retention, operational functionality and effectiveness, and cost efficiency. 

I have listed the six pillars from SAMHSA below, along with my attempt at (extremely) high-level and brief descriptions tailored to those of us working in analytics. I am still on my own learning journey. 

1. Safety: Prioritize physical and psychological safety in all elements of the workplace. In analytics, this can mean that people are able to seek clarity, name concerns, and admit uncertainty without fear of punishment or loss of credibility. It can also mean that we respect limits on things like working hours, cognitive load, personal space, and sensory needs.
2. Trustworthiness and Transparency: Build trust through consistent transparency around decisions, timelines, priorities, and changes. Clarity and predictability can reduce uncertainty, prevent reactivity, and stabilize teams.
3. Peer Support: Reduce isolation and barriers to connection to foster peer support within and across teams. This can allow for greater understanding across disciplines and parts of the organization, smoother workflows, supportive relationships, shared problem-solving, and better knowledge transfer.
4. Collaboration and Mutuality: Involve workers in decisions about policies, procedures, tools, standards, and more. Also, when business units and analytics teams better understand each other’s capacities, workflows, complexities, timelines, needs, etc., collaboration might be more smooth, respectful, and productive. 
5. Empowerment, Voice, and Choice: Choice and control are essential for trauma-impacted people. In analytics, empowerment could mean giving workers more agency in defining things like their own scope, workflows, documentation, timelines, training needs, and work arrangements.
6. Cultural, Historical, and Gender Sensitivity: Address systemic inequities and promote diversity, equity, and inclusion. Design systems from the start to acknowledge, understand, and respect differences. Do not rely on people to constantly identify, overexplain, or advocate for their needs.

Integrating TIC is a deep, long-term commitment that isn’t about checking boxes, a quick workshop, or adding a few supportive practices. It requires honest and sustained cultural and structural assessments, learning, planning, and shifts, and a more balanced distribution of power. But with a new reframing, maybe we can begin to view:

• Workers as human, collaborators, creators, and both autonomous and interdependent 
• Leaders as human, coordinators, facilitators, coaches, guides, and anchors
• Work as collective, learning, growth-oriented, and sustainable 
• Technology as supportive, enhancing, synchronizing, and shared 

This isn’t meant to be a silver bullet, and I know there are many other challenges in analytics that involve data, tools, processes, and more. It may also seem overly idealistic in our current systems. But I feel like tech is at a precipice, especially in the rush toward AI creation and adoption. We’re already seeing increased exploitation of labour and the environment in the AI space, without consideration of short or long term consequences. If we don’t care to stop and make our systems more sustainable, ethical, equitable, and accessible now—what does this mean for our (very near) future? 

I’m curious about what a different approach to analytics work might bring:

• Will we have the space to maintain our health, relationships, and lives outside of work?
• Will relationships within and between teams become more stable, empathetic, and productive—especially between analytics and business units?
• Will we have more space in between deliverables to recover, reflect, and refine our systems?
• Will our products become clearer, more cohesive, more aligned, actually used, and have impact?
• Will we feel safe and supported to show up at work in our own unique ways?

The post Analytics Products Will Never Be Truly Human-Centered Until the Workplaces Behind Them Are appeared first on Nightingale.

“LA on the Move”, our exhibition organized by Metro Art at Union Station in Los Angeles, California, opened in October and will remain on view through next year. Through larger-than-life graphics, a massive 3D map, playful character designs, and even animal sounds, we’ve created an immersive experience that asks Angelenos to see themselves reflected in the lives of coyotes, mountain lions, monarch butterflies, red-tailed hawks, and california kingsnakes.

From City Animals to Union Station

The seeds of “LA on the Move” were planted at ArtCenter College of Design, where we first encountered the City Animals class taught by Santiago Lombeyda and Ivan Cruz. “It was a topic I hadn’t really thought about before,” Jen recalls. “The interaction of humans and animals in LA County—it was super intriguing. The more we got to know the projects and the students, the more excited we became.” Then there was a chance to have an exhibition that pulled together a lot of these concepts and also showcased the student work, created in association with Metro Arts at Union Station. From there it just started rolling”.

The final projects from the City Animals class focused on speculative projects that explored how Angelenos could redesign their homes, backyards, and neighborhoods to better integrate with the natural world. Jason explains, “The projects that the students did were really about how people in LA could think about the intersection of the built environment—their homes, their yards, their backyards—with the natural world”. From there, we led two intensive workshop sessions with the students, working side by side to visualize ecological data in bold, accessible ways that were displayed in the ArtCenter student center for the following month.

From there, we were connected with Arroyos & Foothills Conservancy, a non-profit organization focused on preserving and restoring natural open spaces and wildlife habitats. They became an essential partner, sharing datasets on animal sightings, migration patterns, and habitat corridors across LA County as well as expert advice and access to Southern California’s environmental researchers.

The research process: Data meets daily life

“I think the first thing that we did, and what we always do, is begin with research,” Jen explains. “but in time, we leaned on the expertise of our friends at Arroyos & Foothills Conservancy—they were incredibly helpful. The other part, I think that’s very important, is collecting anecdotal information when you’re talking to people that live in Los Angeles about their experiences”.

For us, stepping away from the data is essential. “It’s important to step away from the facts and the figures, and start talking to people because the experience that Californians have with wildlife is completely different than a New Yorker’s,” Jen says. “You can’t just go about your business like a city dweller and ignore nature in California. It’s integrated into your day-to-day experience”.

Los Angeles, we discovered, is extraordinary in its biodiversity. Jason notes, “Los Angeles has such a unique environment. And what we found is that it’s actually one of the three areas in the world that is considered a biodiversity hotspot“. This became a cornerstone of the exhibition—LA isn’t just a city with some nature on the edges; it’s where wildness lives alongside urbanity in remarkable, sometimes precarious, ways.

Five animals, five stories

We chose to focus the exhibition on five species: coyotes, pumas (mountain lions), red-tailed hawks, california kingsnakes, and monarch butterflies. Each animal became a character in the larger narrative of LA residents navigating neighborhoods, dating scenes, commutes, and survival just like the humans around them.

“One of the first things that you drew was the coyote that says: ‘I love LA.’ That’s one of the featured images in the show,” Jason recalls. For Jen, this illustration became a statement of intent: “A human says, I love LA—and we all know this phrase—but animals live there too. What’s their role in this? So, we wanted to make sure that the animals and humans get equal time in this show”.

The personification of the animals was deliberate and humorous. Jen explains, “The more you learn about animals, how they’re mating with other animals, for instance, you think about the LA dating scene, and then you think about animals, which have some funny crossovers. As we have these neighborhoods in a city, they also have their neighborhoods.” Jason chimes in, “For example, a monarch butterfly says, ‘Hey babe, let’s overwinter in Mexico’—a line that could just as easily come from an Angeleno planning a winter getaway…” Jen adds, “And the monarch is saying like, I’ve got a really busy schedule.” Jason elaborates: “They have this multi-generational migration habit where up to five generations of butterflies will go from Central Mexico all the way up to Nova Scotia and Southern Canada and then back again. And they do this over five different generations. Even more remarkable—five generations later they’ll return to the same tree”.

The California kingsnake became another favorite. “Well, it’s not an LA Dodgers hat. Thank you very much,” Jen jokes, describing the snake’s illustrated headwear. “It’s a Los Angeles hat”. The kingsnake’s ability to live almost anywhere—from woodland to wetlands to suburban basements—made it a perfect symbol of LA’s adaptability. As we say, “you live in my backyard.”

Navigating the hard truths

While humor runs through the exhibition, we didn’t shy away from difficult realities. Rattlesnakes, for instance, posed a design challenge. “I made this drawing. When you might be on a hike, you may encounter a rattlesnake. And this is frightening, right?” Jen recalls. “There was like a discussion about making the rattlesnake so it wasn’t so intimidating, which was funny because I was like, well, a rattlesnake is intimidating and very scary, and you can’t really take animals and smooth out all the rough edges, right? Because that’s not what they are”!

The story of P-22, the famous mountain lion, underscored the fragility of human-wildlife interactions. Jason reflects, “Take the story of P-22—a famous mountain lion that was known around the Mount Wilson Observatory. And eventually, through a series of interactions with humans (and despite best intentions) he dies”. The exhibition addresses this directly, including data on rat poison’s devastating impact on mountain lions and the importance of hazing techniques—like carrying a can filled with coins—to maintain healthy boundaries.

“Even though we anthropomorphized the animals, we shouldn’t forget the fact that there are negative results of some of our interactions with the animals. We should be mindful of that”.

Making data visible and inviting

One of our core practices is taking complex datasets and transforming them into visuals that invite exploration rather than intimidation. “Part of what we do is find information and basically make it much more understandable to the general public and to ourselves,” Jen explains. “Like rat poison killing pumas, right? We made this diagram so that we have the data there, but you can just see it more clearly”.

A standout piece in the exhibition is the massive chart “Animal Species at Risk in California”, which visualizes 930 species by class and phylum, showing which are extinct, endangered, or imperiled. Working with data visualization collaborator Paul Buffa, we transformed this overwhelming dataset into the shape of a California poppy—the state’s native flower.

“If I saw this information in spreadsheets, I would be very intimidated because it’s just a lot of information,” Jen admits. “But since we put it into this California poppy, which is a native plant, it invites you over to explore it. You don’t have to look at every single detail, but it is fascinating”.

The wall also includes a Sankey diagram comparing California’s at-risk species to global standards—revealing that California has considerably more species in danger. And the bar chart showing imperiled species? “It literally towers over your head. It’s about seven and a half feet tall, so we wanted it to have a physical relation to how you encounter the data”.

The iconic title wall: Observing Union Station

The exhibition’s title wall features three illustrated characters walking across a vibrant gradient backdrop—each carrying something that subtly references animal behavior. Jen describes how these characters emerged: “We were standing in Union Station, and I could see people walking through, going from the trains to the entrance, and it gave me this idea about what kind of people would be walking through LA and walking particularly in Union Station”.

The older gentleman carries a bag of groceries, echoing how animals travel to forage and transport food. The young woman holds a bundle of flowers, referencing seed distribution—how seeds attach to animal coats or are eaten and deposited elsewhere. “All said and done, the more time you spend with the exhibition, you know every element is intentional and thought out and has a relationship to the information that we learn as we go along,” Jen explains.

The massive 3D map: Placing yourself in the data

Perhaps the most captivating element of LA on the Move is the enormous 3D map, created in collaboration with Julian Hoffmann Anton. This wasn’t just a cartographic exercise—it became a months-long process of negotiation, expansion, and refinement.

“Every project we do, we discuss a map component,” Jen says. “And sometimes we have time to do it, and sometimes we don’t because what starts as a simple map becomes very complex. It’s because a map is political. You can’t leave anyone off because they’ll notice”.

Initially, the map focused narrowly on downtown LA and Union Station. But through conversations with Metro Arts staff and community input, it expanded dramatically—eventually encompassing all of LA County and parts of Orange and San Bernardino Counties. “We were pushed and pushed on the map, but that’s not a bad thing. It’s a much more inclusive map, so when visitors come to Union Station, they can find themselves”.

In addition to showing every detail of the city, the map tracks sightings of all five featured species across the region, revealing fascinating patterns. Mountain lion sightings appear surprisingly far south of downtown; California kingsnakes cluster in parks and mountains but occasionally show up near Marina Del Rey; while coyote sightings may reflect research centers as much as actual populations.

“I’ve never seen a map of this scale, physically, of this detail,” Jason marvels. “It’s an extremely detailed 3D rendering of the entire metro area”. And because it wraps around a corner, visitors can find neighborhoods that might have been cropped out of a conventional map. Jen describes a photograph of a man pointing to the side panel: “He’s finding himself, which we wouldn’t have had in our original idea”.

Adding Sound: Activating the Space

For the first time in a Data Vandals project, we incorporated audio. “I pushed for this because we wanted to activate the space as much as possible,” Jen says. “We’re dealing with walls, and we wanted ways to expand these rectangles out”.

Visitors can hear the sounds of pumas, coyotes, and hawks. “I thought, okay, if I’m walking through Union Station, what is it like to hear some of these animals?” Jen explains. The sounds are surprising—sometimes beautiful, sometimes unsettling. Jason describes, “The mountain lion has lots of really low growls, more aggressive than a purr, and I found those to be unsettling”. Coyote calls also sound strange and a bit frightening, but these sound elements ground the exhibition in sensory reality, reminding visitors that these are not cartoons but living, breathing neighbors.

Iconic cutouts and LA signage culture

Atop each wall, we placed large cutouts of the animals lifted high on Sintra board to add height and visual drama. Jason says, “We wanted them to refer to the history of the Hollywood back lot, even the Hollywood sign itself.”

Jen reflects on LA’s distinctive signage culture: “I think the signage is very different from anything you ever really see on the East Coast; in New York we don’t have that kind of sign culture and I find it fascinating and really attractive”.

The billboard aesthetic also responds to Union Station’s architecture—a stunning 1930s Art Deco space with soaring ceilings and intricate tilework. “Union Station is so gorgeous, you want to try to do it justice. Something that iconic, you worry that whatever you do is going to be overwhelmed”. To honor the building, we photographed the tile floors and extracted colors to integrate into our palette, creating a dialogue between the historic architecture and our contemporary street-style graphics.

As the exhibition settles into its year-long run, we hope it becomes a recurring destination; a place where commuters pause for five extra minutes, where families return to discover new details, where Angelenos see their neighborhoods reflected in a 3D landscape populated by shared species.

“I just want people to enjoy it and have fun with it and see themselves in the data,” Jen says. “It’s so fun to see the different types of people, and I feel like I could draw those people and put them into the exhibition. It reflects a lot of our intentions”.

Jason hopes for depth and revisitation: “I’d love that the exhibition is very detailed; you can return to it over and over and learn something new each time that you revisit it”. And Jen adds with a laugh, “I hope it brings us back to California again and again –  we love LA “!

“LA on the Move” is on view at Union Station through 2026.

For more information: https://datavandals.com/la-on-the-move.

The post LA on the Move: Data Vandals Bring Wildlife and Humans Together at Union Station appeared first on Nightingale.

We both work full-time and raise two young children, so our evenings are the only moments left for connection. He is the hardest-working person I know—relentless in chasing a dream that has recently taken shape as a tech startup. His quietest, most productive hours begin when the kids sleep. I, on the other hand, go to bed alone.

At first, I texted him, “Good night.” He would reply hours later, sometimes with a simple “coming to bed now.” But after two months, I realised something: I had data. It’s not formal, tidy data- casual timestamps, scattered messages, and a discernible pattern.

So, I started tracking it deliberately. A spreadsheet. One column for the time I went to sleep, one for him. If I forgot to log it, I would ask him in the morning: “When did you come to bed?” He always knew I was tracking his bedtime time. It comforts me. Data gives shape to ambiguity. It holds stories quietly, waiting to be told.

There is a particular calm that comes with collecting data. For me, it is a form of emotional processing. Tracking our sleep was something small I could hold onto. A private system of meaning. Each entry felt like a whisper to myself: “You are paying attention”.

As the dataset grew, so did the emotional tension. We argued. Not because I didn’t support him, but because I missed him. Missed us. One night, I added a third column: fights. Eventually, a pattern emerged. We always argued at the start of the week after the weekend reset. I brought it up, along with the spreadsheet, and together, we coined the Sunday-In-Sync Rule. Once a week, we would meet in the middle. He would wrap up earlier, and I would stay up later—a small act of reconnection in a sea of drift.

At one point, I realised that the actual times we went to sleep didn’t matter as much as I thought. What mattered was the delta—the difference between them. That delta became the emotional signal, more about how far apart we were. From that moment on, I shifted my perspective on the data and how I wanted to visualise it. Each petal would represent a single day. My sleep time became the baseline. His would be expressed as a distance—the space between us.

Five months in, the dataset had become too complex to keep in rows and columns. I started sketching. One flower for each week. Some flowers bloomed, representing nights spent together, even if late. Some wilted, marking long gaps between bedtimes. A visual garden of our sleep patterns emerged. A bouquet of data is carefully drawn.

Why flowers? Because they are the most classic romantic gesture. A universal symbol of affection, apology, and devotion. I was not just visualising data; I was creating a love letter. One that said, “I see you. I miss you. I’m with you”. Presenting the data as a bouquet felt right. It framed the tension with tenderness.

Drawing it brought another kind of clarity. Unlike the spreadsheet, which was linear and clinical, the floral format made space for nuance and softness. It became a way to honour the emotional weight of these minor, repetitive, daily differences. A quiet ritual that helped me come to terms with what I could not control.

When I showed him the finished visual, he was speechless. Then smiling. Then laughing. He told me I was the most supportive wife he could ask for. Later, I found out he had shared the piece with his friends. That meant everything to me. He saw what I saw.

Is it a visual love letter? A quiet protest? Maybe both.

What I know is this: in tracking our distance, I found a way to feel closer. I supported him in silence, witnessed his effort, and honoured the rhythm of our parallel dreams.

The post A Garden of Sleep: Tracking the Emotional Distance Between Two Bedtimes appeared first on Nightingale.

Bicultural individuals, second-generation migrants, nonbinary genders, mixed-race people, and hybrid language speakers are identities that fail normative categorisations systems. These identities cannot be easily datafied and such a digital failure is usually conceived as an opportunity for antisystemic exploration. Athough aware that data representation is not always necessary and desired by minoritised groups, this article proposes nonbinarity as a feminist analytical lens that overcomes the rigidity of opposite identity categories. Nonbinarity challenges the concept of residuality—the idea that certain identities cannot be neatly categorised within systems based on exclusive nominal distinctions, due to their inherently mixed or overlapping nature. Rather than enforcing dichotomies, nonbinarity turns categories into coexisting continuous variables that generate a spectrum of nuances, thus welcoming both unitary and mixed identities. This systematisation derives from merging gender pluralism with social identity theory, viewing gender as one among many social identities constructed through group affiliations, and recognizing that individuals may simultaneously relate to seemingly opposite identity groups.

The Gender Diamond

To be clear with terminology, this article is rooted on gender, which is the psychological, social, and behavioural aspects of a person’s sexuality (e.g., woman, nonbinary, man). Consequently, it dismisses sex—the more physiological and bodily counterpart (e.g., female, intersex, male)—orientation—the kind of people the individual is attracted to (e.g., gynophile, androphile, pansexual, demisexual)—and the combinations of these dimensions (e.g., gay, transgender, genderfluid).

Gender pluralism defines gender as a social construct built performatively through a semantic layering of meanings that define gender groups. This layering is not necessarily coherent but may mix meanings attributed to different genders and change over time, moving around a nuanced semiotic structure known as the gender spectrum. The gender spectrum is thus a continuous plane of meanings on which Abrahamic traditions have imposed the binary categories of women and men by assuming that the reproductive schema of the human species could also be a gender schema. However, nonbinary genders such as androgynes, demimen, genderqueer, or agender people, exist in the cracks between these binary categories. 

According to the dual identity approach, the gender spectrum is understood as a bidimensional space defined by two independent continuous variables: femininity and masculinity. For instance, a binary woman typically scores high in femininity and low in masculinity; an agender person scores low on both; and an androgyne expresses varying degrees of both traits without fully aligning with either. Importantly, femininity and masculinity are not objective or fixed qualities—they are intersubjective, shaped through social interpretation and context.

Based on the dual identity approach, the Gender Diamond is a visualisation of the gender spectrum designed to describe gender identity in computational terms—which are data. Thanks to the two gender scales, nonbinary and binary genders live together in the same space. Furthermore, multigender identities (i.e., those identities that shift between genders according to contexts, e.g., bigender people) can be represented in the Gender Diamond as the user can select multiple locations. This visualisation emerged from a codesign approach that valued the contributions of both gender-diverse individuals and experts. Furthermore, it has been recently improved through an extensive literature review of various psychometric instruments for the assessment of gender identity as well as a quantitative study involving more than 450 Italian-speaking individuals (article coming soon).

Designing new Identity Diamonds

Social identity theory posits that individuals define themselves through their affiliations with social groups, such as those based on nationality, religion, social class, or gender. When combined with the insights of gender pluralism, it becomes clear that these social identities are not static or inherent, but performative: they are configured through the layering of the meanings that define each social group. Then, this layering of meanings often fails perfect coherence, performing behaviours of opposite groups, thus creating mixed identities. As androgynes challenge the woman/man antinomy, second-generation migrants cannot be reduced to the local/foreigner binary and commuters do not fit the urban/rural dichotomy.  Going back to categorisation systems, mixed identities constitute a social residuality because they do not univocally fit a single box, as clearly emerged in the South African apartheid system. 

This article proposes nonbinarity as a conceptual key for framing—and thus datafy—not only the gender spectrum, but also other identity spectra defined by the coexistence of opposing references. As gender nonbinarity subverted the two nominal categories of women and men into the continuous variables of femininity and masculinity, the USA-Mexican mestisa can be mapped in an Identity Diamond framed by Mexicanness and Americanness. Similar semantic operations can be envisioned in other borderlands or, more broadly, in any context shaped by the blending of two cultural, linguistic, or national populations.

To illustrate this point, this article presents a Language Diamond of South Tyrol, a region on the Alps with tensions between German and Italian affiliations because, although its Germanic history, it was assigned from Austria-Hungary to Italy after the First World War. Additionally, this article also introduces a more general Belonging Diamond, which represents the simultaneous experience of feeling both local and stranger in relation to a place. These models aim to capture the layered and nonexclusive nature of identity as it unfolds across social, cultural, and geographic boundaries. It is important to notice that the labels on such visualisations are not incompatible nominal categories—each referring to a different construct—but placeholders that refer to varying intensities of the same idea. Also, these placeholders are not fixed to a precise value but rather suggest a predominant association, thereby acknowledging a field of alternative interpretations around them. Their point is not to enforce rigid boundaries, but to orientate the navigation of the spectrum as the identity evolves through time.

Nonbinarity and intersectionality

The relevance of such nonbinary datafication becomes especially clear in discussions of data violence—the structural erasure of identities that don’t fit into neat categories, making them unintelligible to our digital, political, legal, and scientific infrastructure. As data feminism argues, in our data-driven society, only what is properly categorised and quantified actually counts. Therefore, developing feminist lenses to frame identities is crucial to spot patterns of discrimination.

In particular, intersectionality means recognising such discriminations across different types of social identity by combining their traditional categories. For example, intersectionality understands the conditions of black women (i.e., gender: woman/man + race: black/white) or disabled Muslim people in western countries (i.e., ability: able/disable + religion: Christian/Muslim). Intersectionality is therefore an intersemantic lens: it analyses how different types of identity intersect. In contrast, nonbinarity has an intrasemantic approach: it operates within a single identity type to challenge the binary logics of its taken-for-granted categories. Together, these two approaches provide complementary strategies for exposing and addressing structural inequalities.

To conclude, the data that can collected through our instruments reflect—and inevitably simplify—specific aspects of a person’s inner experience, which is itself fluid and hard to grasp. While such datafications can offer valuable insights, it should not be used extensively and uncritically. Data should be used not as a means of control, but with the deliberate intent to respect, protect, and, where possible, empower those it seeks to represent.

The post Datafying Mixed Social Identities: Nonbinarity as the Complementary of Intersectionality appeared first on Nightingale.

This year at VISAP—a mini conference and exhibition exploring the intersection of data visualization, art, and science—we’re asking what it means to approach visualization not just as a practice of analysis and synthesis, but as a form of collective care. How do we design visualizations that not only represent, but also actively protect, nurture, and respect the environments and communities embedded within datasets? What practices emerge when we begin to visualize data with thoughtfulness, empathy, and intention? In a time when data shapes public perception, policy, and personal identity, centering care in our visual methods becomes not just desirable, but essential.

In the world of data visualization, “care” is not a term we use often. We usually talk about clarity, insight, and impact; terms that suggest objectivity and utility. But as datasets expand to reflect our bodies, beliefs, environments, and communities, and as algorithms increasingly shape our collective realities, the visual representation of data becomes an act reflecting politics, culture, and ethics. It shapes how we understand one another and the systems built around us. In this light, the role of the visualizer extends beyond aesthetics or clarity, calling for a deeper engagement with social consequence and ethical responsibility. Recognizing this role means accepting that visual choices can influence narratives, reinforce or challenge biases, and shape public understanding in lasting ways.

In contemporary digital culture, data functions not as a static artifact but as a living archive, one that holds memory, identity, and collective history. Biometric scans, environmental sensors, and geotagged images—nearly every aspect of human life today is captured and converted into data. Giorgia Lupi suggests that working with data can uncover deeper connections, revealing not just patterns in the world, but insights into what it means to be human. Her approach invites us to see data not as detached or abstract, but as deeply embedded in the stories, emotions, and lived experiences of individuals and communities.

Within this context, data visualization is not merely a cosmetic tool for representation, but a critical process of reinterpretation, contextualization, and communication. It offers a means to narrate our datafied collective histories, shaping how communities are made legible. Artists and designers working in data visualization act as communicators and storytellers. Through visual, sonic, spatial, or even olfactory forms (such as scent-based installations) they transform abstract data into something tangible, something we can feel, question, and connect with. In doing so, they turn datasets into living archives and visualizations into spaces for reflection, empathy, and care.

Maria Puig de la Bellacasa calls this orientation “matters of care.” It’s a call to move beyond surface concern and into the thick, entangled, affective labor of maintenance, repair, and relationality. It’s an invitation to care for our practices the way we care for each other—not just efficiently, but attentively and critically. Within this framework, care is not about sentimentality; it is a relational and communal ethic. It urges us to take responsibility for the data we engage with and to honor the lives, communities, and ecosystems it represents. To visualize with care is to visualize with empathy: to make visible environmental harm, surface suppressed narratives, reveal shared experiences, and confront the structural biases that too often remain hidden.

Building on this understanding of visualization as a relational and embodied practice, we envision a future in which data visualization becomes a process of restoration, connection, and long-term social resilience. This vision invites us to approach data as a space for healing, resistance, and belonging. It encourages the use of data visualization to support the well-being of both environments and the communities most affected by them. These same values must also guide how we collaborate with emerging technologies, especially as we begin to co-create meaning with algorithmic systems and AI—for example, by shaping how data is interpreted, narratives are generated, and decisions are guided by machine learning tools. This collaboration raises critical questions around authorship, agency, and ethics: Whose data is used? Whose voices are amplified or erased? A care-centered approach to AI foregrounds transparency, accountability, and relational design, prioritizing systems that are socially responsible and culturally aware.

VISAP ’25 explores the theme Collective Care, inviting bold, critical, and creative works that reflect on the role of visualization in an interconnected world. In conjunction with IEEE VIS 2025, the program welcomes papers, pictorials, and artworks engaging with care, solidarity, and ethical collaboration. VISAP will take place in person at the University of Applied Arts Vienna from November 6–15, while IEEE VIS runs at the Austria Center Vienna from November 2–7.

Submission deadline: June 13, 2025.

For details, visit: https://visap.net
LinkedIn: https://www.linkedin.com/company/ieeevisap/
Instagram: https://www.instagram.com/visapnet/
Contact: art@ieeevis.org

The post Visualizing as a Form of Collective Care appeared first on Nightingale.

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Business leaders once championed dashboards as windows into organizational performance, but they became too familiar, too technical, and the value diminished. As evidence, one look at the relationship between those with roles in “business intelligence” in comparison to the business leaders they serve shows the massive gap in seniority, influence, and wages.

How did this happen? Let’s discuss these 3 ideas:

• Dashboard rot devalued BI
• Data people were never trained in design or communication
• D3.js is complicated

Dashboard rot devalued BI

Business leaders scrambled to use data to inform the C-suite, and in the process, multiple layers of the organization had their own dashboards. When BI software became a premium license, it was only a matter of time before enterprises began counting which dashboards were used and which had never been used. The overwhelming under-utilization of dashboards across an organization led to the term “dashboard rot” which is a fundamental misunderstanding of what the value was in the first place. It’s like counting all Word documents in an organization vs what is published. The value has always been in the insight, not in the number of documents.

The way BI software was monetized ended up devaluing its own importance. Dashboards became an IT cost-center in many regards instead of a strategic advantage. It became a burden in the organization, and in many organizations, “reporting” was seen as boring and a potential waste of time.

Thinking of the value of BI differently, if a dashboard can make a $1M decision easier, is it worth $1M? If, over its lifetime, it supports a $5B company for running its business daily, does that still make it worth $1M or more? On the contrary, organizations don’t think of investing in software in the same way: software is a strategic advantage, but dashboards are just the cost of doing business.

Data people were never trained in design or communications

Maybe part of the reason why dashboards instill a certain amount of hesitation is because most are not well designed. Many people working in analytics come from data science, data engineering, or data analysis backgrounds, and those fields lack significant design or communications training. While it is impossible to say all dashboards are badly designed, I’m certain that most people who create dashboards do not consider themselves to be “good designers.”

There’s a big difference between the kind of high-level graphic design we see in advertising or in consumer apps and the kind of important tweaks that could easily elevate most dashboards. In fact, most dashboards can probably get a significant lift by adjusting the language used in titles and labels alone.

The success of data literacy programs proves the importance of training people in more than just foundational data visualization practices.  This shift—if we can make it one—from data towards communication might see the value returned to business intelligence, ushering in a new generation of thought partnership between analytics professionals and organizational leadership.

D3 is complicated

The reason why BI software exists is because custom coding charts was difficult. When D3.js was invented, an entirely new way to draw shapes in the browser created new opportunities to visualize data from simple charts to multidimensional interactive tools. But developing charts with D3.js was far from straightforward and pushed it into the domain of software development.

While it is not the fault of D3 that dashboards have lost their zest, the complexity of doing this work opened the door for faster (and therefore cheaper) tools to take its place. Many frameworks to create interactive charts for business sprang up each with their own tradeoffs, each focused on their own flavor of front-end, and in the process, the software design was assigned to the UX designer. I’m a former UX designer, and I can tell you definitively that data visualization and data communication simply does not exist in user experience design—despite the fact that almost all software design is a visualization of data.

Maybe it’s time we drop the idea of dashboards and focus instead on data communication? By adopting this shift we might just recontextualize the power of data.

There’s a lot here to discuss, so please let me know what you think!

This article originally appeared at: https://www.linkedin.com/pulse/word-dashboard-broken-jason-forrest-agency-aco1e

The post The “Dashboard” is Broken appeared first on Nightingale.

Identity is rarely fixed or mutually exclusive; it is best understood as a “check all that apply” framework. For instance, gender is a fluid construct that encompasses diverse identities, from evolving pronoun use to cultural perspectives like the Fa’afafine of the South Pacific, who are recognized as a third gender. Similarly, race and ethnicity defy strict categorization, as individuals often identify with multiple racial groups or cultural heritages, such as being both Hispanic and Asian. Sexuality also resists rigid boundaries, with terms like “Men who have Sex with Men” (MSM) highlighting behaviors that challenge traditional sexual identity labels. Simplifying these complexities through vague categories like “Other” erases meaningful distinctions and undermines efforts to capture actionable insights. Embracing this fluidity allows for analyses and visualizations that reflect the richness of human diversity.

These challenges are heightened by a shifting sociopolitical climate that has seen efforts to dismantle diversity, equity, and inclusion (DEI) initiatives. Critics of DEI efforts frame them as discriminatory, while supporters argue they are essential for addressing systemic inequities and fostering inclusivity. As such, it is more important than ever to rethink how identity is analyzed and represented in data.

Visualizing identity presents unique challenges, particularly as the number of identity categories increases, leading to exponential growth in potential combinations. By leveraging flexible data categories and creating models that reflect the dynamic nature of identity, data visualization can play a critical role in advancing equity and inclusion. Thoughtful visualization highlights the multifaceted realities of human lives, providing a powerful tool to foster more equitable practices.

Challenges in visualizing intersectionality (Statistical, ethical, and practical)

Intersectionality is vital to socially focused research because it reveals how unique experiences emerge from the intersections of identities, rather than from individual categories like race or gender in isolation. For instance, the experiences of an African American man and a homosexual man differ, but the lived reality of an African American homosexual man represents a distinct combination of challenges and opportunities that cannot be fully understood by examining either identity alone.

Incorporating intersectionality into research and analysis is crucial for designing inclusive and equitable programs and policies. By understanding how identities overlap, researchers can better address the nuanced needs of diverse populations. However, applying intersectionality in practice is fraught with challenges. Translating its complexity into actionable analysis and effective communication is particularly difficult in fields such as social services and public policy, where clear and practical solutions are often prioritized over nuanced representation.

Over the years, I have explored the academic literature, engaged with professional communities/listservs, and even turned to popular forums like r/dataisbeautiful in search of effective ways to visualize and represent intersectional data. Despite these efforts, there is no single solution that fully captures the complexity of intersectionality in a way that is both accessible and clear to a broad audience.

Instead of offering a definitive solution, I will highlight the key challenges involved in working with intersectional data and explain how these difficulties arise during data analysis and storytelling. These challenges can be categorized into three main areas: Statistical Modeling, Research and Reporting Ethics, and Visualization. Additionally, I will share my own imperfect attempts at visualizing intersectional data, with the hope that they inspire further exploration, discussion, and innovation in the field.

Statistical modeling. In quantitative research, studying intersectionality means understanding how overlapping social identities combine to shape experiences, rather than analyzing them separately. Traditional approaches often rely on statistical models that measure how different identity categories interact. For example, instead of looking at gender and race as separate influences, these models examine how being a woman of color might have distinct effects beyond simply adding up the effects of gender and race individually. However, these methods often assume specific types of relationships that may not fully reflect real-world experiences, particularly in public health research.

Newer approaches, such as decision trees and multilevel modeling, offer alternative ways to analyze intersecting identities. Decision trees work by splitting data into smaller groups based on key characteristics, helping to identify patterns among different subgroups. While useful, these trees can sometimes create overly specific divisions that may not apply to other datasets.

Multilevel models provide another way to explore intersectionality by separating the effects of broader social categories (such as race or socioeconomic status) from individual differences. This helps researchers understand how much of an outcome is shaped by group identity versus personal factors. A specific type of multilevel modeling, known as MAIHDA, is particularly useful for examining inequality at both individual and group levels.

Each of these methods has strengths and limitations. While no single approach fully captures the complexity of intersectionality, they offer valuable insights into social inequalities. Given their complexity, effective data visualization is essential to ensure that findings are clear and accessible.

Ethical. The ethical dilemmas surrounding intersectional data are even more complex. In one rural initiative I worked on, we collected about 150 responses, but only one respondent identified as an African American transgender female. Including their data in community-based reporting would effectively de-identify them, violating anonymity and confidentiality.

To address this, some teams employ data suppression, removing such entries from the analysis altogether. While this protects individual identities, it comes at a significant cost. By excluding these voices, we perpetuate the marginalization of underrepresented groups and fail to fully understand the diversity within our communities. Even when such entries are aggregated into an “Other” category, the result is often equally dissatisfying and uninformative.

This ethical conundrum—whether to prioritize the protection of individuals or amplify their voices—is difficult to resolve. The Belmont Report emphasizes Respect for Persons, but how do we best show respect? Is it through safeguarding identity or ensuring representation in the analysis? This question becomes even more fraught in today’s political climate, where DEI initiatives face direct hostility. Informed consent seems like a logical solution, but it is often too simplistic to navigate these nuanced challenges.

Visualization. The final problem of intersectionality is most relevant to this journal. To date, I have experimented with several different visualizations to show the relevant partners not just what the demographics of the population are, but how these populations intertwine. Here, I review these attempts and where they sometimes help, sometimes hurt, but collectively fall short.

Exploring visualization techniques (Strengths, limitations, and innovations)

I have a strong interest in behavioral health, and this paper provided an opportunity to explore the Treatment Episode Data Set (TEDS-a), a national database maintained by the Substance Abuse and Mental Health Services Administration. TEDS includes episode-level data on substance use treatment admissions (TEDS-A) and discharges (TEDS-D), with data collection starting in 1992 for admissions and 2000 for discharges. The datasets offer demographic information (e.g., age, gender, race/ethnicity, employment status) and substance use characteristics (e.g., substances used, age at first use, route of administration, and frequency of use). Data are sourced from facilities receiving state or federal funding, though reporting requirements vary by state. Notably, “gender” in this dataset is defined only as “Male,” “Female,” or “Missing/Unknown/Not Collected/Invalid,” a challenge I take issue with for reasons discussed earlier. Nonetheless, this is the reality of working with this dataset.

All the figures were created by me in R, using various data transformations followed by ggplot2 for visualization, except where noted.

Bar plots

Bar plots are a simple and effective way to display variations within a population, particularly when combined with faceting to show multiple subgroups. Using percentages on the y-axis, rather than raw counts, ensures a consistent scale across facets, making it easier to compare patterns between groups.

However, one of the significant limitations of bar graphs in this context is the issue of double-counting (e.g. a person can be “White” and “Mexican”). When individuals belong to multiple categories, their representation can appear in more than one bar, which can distort the interpretation of the data. Additionally, while bar graphs can highlight distributions within categories, they fail to capture the intersections between them. This limitation means that crucial insights about overlapping identities or relationships between categories may be overlooked.

To address these challenges, it is essential for data analysts to clearly explain the graph’s purpose and limitations, ensuring that viewers understand what the visualization represents—and what it does not. Balancing simplicity with clarity is key to ensuring the graph remains both interpretable and informative.

Sankey diagrams

Sankey diagrams are powerful tools for visualizing flows and relationships between categories, particularly when exploring how larger groups break down into smaller subcategories. Traditionally, Sankey diagrams are used to depict processes like budget allocation or pathways, such as the post-high school decisions of a graduating class. However, they are also highly effective in representing intersectionality by illustrating how individuals in one category connect to those in another.

In the context of this analysis, this Sankey diagram visualizes the flow from sex to race and then to ethnicity. This sequential approach highlights the distribution of individuals across these intersecting categories, offering both a numerical and visual understanding of how these groups overlap. The decision to start with sex and then move to race and ethnicity was intentional, as this order begins with the category with the fewest options and progressively adds complexity. Including age was avoided in this diagram to prevent visual clutter and maintain clarity, though this decision was somewhat arbitrary.

By examining this visualization, we can see how much of a given category flows into its subcategories, revealing patterns that may not be immediately obvious from raw data alone. This approach makes it easier to identify disparities, overlaps, and relationships among different identity groups, providing a clearer picture of intersectionality.

I didn’t use R to make this graph. I’ve tried in the past to use various packages but have never been great at correctly structuring the data and then implementing the code. What I have found is the highly useful sankeymatic.com website, a no-code way to implement these diagrams.

Cross-tabular plots

This final visualization is the one I personally prefer, though it is not without its challenges. Instead of using a traditional correlation plot, I opted for a simpler yet powerful approach: cross-tabular summaries. These tables provide a clear and straightforward way to examine how categories intersect without overcomplicating the visualization. The first plot represents a cross-tabulation of race and gender, with the cells displaying the percentage of the overall sample. This approach allows for a quick and intuitive understanding of how these two categories overlap and their relative distributions within the dataset. I used three decimal places because some of these categories are small.

For the second visualization, I extended this approach to incorporate a third dimension: ethnicity. Using faceting, I created separate cross-tabulation plots for each ethnic group, with the cells now representing the actual counts rather than percentages. This decision was intentional, as it emphasizes the real-world implications of the data. By showing the actual number of individuals within each category combination, it serves as a powerful reminder that these are real people who sought treatment, each with unique needs, experiences, and stories.

While this method somewhat avoids the pitfalls of overly complex 3D visualizations, it has limitations. Adding a third dimension through faceting requires viewers to interpret multiple plots simultaneously, which can become overwhelming if not carefully presented. Additionally, as with any visualization that layers information—whether through percentages, actual counts, or multiple facets—it’s essential to strike a balance between clarity and comprehensiveness. Too many dimensions or overly detailed visuals can detract from the overall message.

Yet, I find that this last approach provides a flexible framework for exploring intersectional data in a way that is both interpretable and grounded in real-world contexts. By starting with a simple race-gender summary and then layering in ethnicity through faceted counts, this method strikes a balance between simplicity and depth, allowing for meaningful insights into the data.

Other unsuccessful methods

In exploring ways to better visualize intersectional data, I’ve experimented with several techniques that, while conceptually interesting, ultimately failed to communicate effectively.

1. Set theory and concentric circles
   Inspired by set theory, I tried using interlocking concentric circles to represent categories and their overlaps, with the overlapping areas displaying intersectional characteristics. While the concept was appealing, in practice, the visualization became very unwieldy. Certain categories were nearly impossible to distinguish, undermining the very purpose of highlighting intersections. Additionally, the coding required was challenging and not worth the limited clarity it provided.
2. Bar graphs of total numbers
   For the sake of thoroughness, I also experimented with a simple bar graph that plotted the total number of individuals within each intersectional category. Unsurprisingly, this method was flawed. Larger categories dominated the visualization, while smaller, marginalized groups were overshadowed or entirely invisible. Instead of highlighting intersectionality, this approach risked perpetuating the same marginalization we’re trying to address.
3. Qualitative narratives
   Another approach I considered was abandoning quantitative visualization altogether. By giving voice to individuals from specific intersectional identities, this method allows them to articulate their unique experiences and needs in a way that no graph or chart ever could. While I deeply value this qualitative approach for its ability to center lived experiences, it doesn’t address the core challenge of intersectional visualization—it simply sidesteps it.

Lessons learned and future directions

A persistent challenge in data analysis arises with “check-all-that-apply” questions, where respondents can select multiple options for a single root question. This is especially true for identity markers such as race and ethnicity. For example, a respondent might identify as Persian, Egyptian, and German, reflecting the nuanced and multifaceted nature of their identity. While this flexibility mirrors real-world experiences, it creates significant challenges for analysis, as the data defy neatly categorized, mutually exclusive frameworks. Some visualization techniques can offer partial solutions by highlighting overlapping identities, but these methods often rely on simplifying assumptions or produce unwieldy outputs that fail to fully capture the richness of intersectional data.

This dilemma underscores a larger issue: how do we effectively measure and communicate the complexity of intersectionality and fluidity? Visualizing intersectional data is inherently difficult because it requires representing the intricate relationships between multiple, interwoven categories. These relationships are not just overlapping but dynamic, changing across contexts and over time. Techniques like cross-tabulations and faceted plots offer some progress, but every additional dimension—whether expressed through color, shape, or layering—increases the risk of a visualization being overwhelming or unintelligible. Yet, we cannot ignore this complexity, as it is precisely what reflects the lived realities of the populations we study.

The Catch-22 of visualizing intersectionality is this: the complexity we aim to convey makes our efforts harder to interpret. Balancing clarity and complexity is a challenge I encounter often and one that requires constant refinement. Adding dimensions to reflect overlapping identities is not a problem to solve but a reality to navigate with care.

There is no perfect solution. Visualizing intersectionality demands thoughtful experimentation, a willingness to embrace complexity, and, most importantly, a commitment to clear and transparent communication. It’s a continuous process of trial and learning, and that’s okay.

The post Beyond the Binary: Improving Data Visualization for Intersectional Identities appeared first on Nightingale.

This is my story—a journey from volunteering on a single project to co-leading a global community of data enthusiasts and changemakers. Along the way, I’ve had the privilege of working with incredible non-profits, witnessing the profound impact of their work, and supporting them with the tools and insights that data visualisation provides. I’ve also seen volunteers—beginners and experts alike—discover new talents, grow in confidence, and find purpose in contributing their time and skills.

Viz for Social Good is more than just a group of volunteers doing data visualisation; it’s a movement. It’s a testament to what can happen when people come together to achieve extraordinary things.

The origins of a movement

The story of Viz for Social Good began in 2016, sparked by a moment of insight and empathy. Chloe, our founder, was attending the Tableau Conference when she had the chance to meet with representatives from various non-profits. Through these conversations, she uncovered a common challenge: while these organizations possessed invaluable data about the communities they served, they lacked the resources and expertise to transform that data into actionable insights.

Non-profits, often operating on tight budgets and limited staff, had to prioritize their core missions, leaving little room for investing in tools or training to visualise their data effectively. Chloe saw the gap and envisioned a way to bridge it. What if data professionals and enthusiasts could lend their skills to these non-profits, helping them unlock the stories hidden in their data?

Today, Viz for Social Good has helped 50 non-profits (check VFSG Champions to see all projects) from around the world, addressing challenges that range from advocating for refugees to combating climate change.

My humble beginning

My journey with Viz for Social Good began like many others—as a volunteer eager to make a difference. In early 2021, I decided to form a new Viz for Social Good chapter in Brisbane, Australia. The Project’s aim was to support Sunny Street, a local non-profit dedicated to providing healthcare to vulnerable populations.

Sunny Street’s mission immediately resonated with me. They weren’t just offering medical services; they were providing dignity and hope to people who had been left behind by traditional healthcare systems. I knew that amplifying their story through data could make a real difference in their ability to secure funding and expand their programs.

I had originally planned to organise a face-to-face event, but due to the COVID-19 lockdown, we had to shift to a virtual event. Despite these challenges, the event was a resounding success. Participants presented visualisations that Sunny Street could use to showcase their impact and advocate for support.

I had strongly encouraged both volunteers and non-profits to be active participants in our Slack channel—asking and answering questions, sharing feedback, and submitting drafts. This active engagement has fostered a significant culture of collaboration for each project.

Additionally, learning from my datathon organization experience at a previous job, I implemented a significant change in our project submission process. Non-profit organizations now review all submissions and select their top five volunteers. These selected volunteers are given the opportunity to directly present their work to the organizations.

This new approach achieves two key objectives:

1. It creates a more meaningful connection between volunteers and the non-profits they support.
2. It ensures that the most valuable and impactful contributions from volunteers receive the recognition and attention they deserve.

After some time, Vanitha, our Executive Director, offered me an opportunity to join the Board of Viz for Social Good as Director of Operations. This was an honor I hadn’t expected, but I enthusiastically accepted. My role recently evolved from Director Operations to Executive Director, taking part of the co-leadership of the movement.

Why non-profits struggle with data

Non-profits play an essential role in addressing the world’s biggest challenges, from poverty and education to climate change. However, their work is often undervalued or misunderstood, partly because they face significant barriers when it comes to data. While data holds immense potential to tell stories, measure impact, and secure resources, non-profits often find themselves unable to fully harness this power.

One of the most common issues I’ve observed is a lack of resources. Non-profits operate on tight budgets, prioritizing direct impact over investments in technology or analytics. Every dollar is scrutinized, and understandably so, as these organizations strive to allocate funds where they are needed most.

The role of Viz for Social Good

These challenges are exactly why Viz for Social Good exists. By connecting non-profits with skilled volunteers, we provide a way for them to leverage their data without diverting resources from their mission. Volunteers bring expertise in data visualisation, storytelling, and analytics, bridging the gap in skills.

Empowering non-profits

The power of data storytelling

At its core, Viz for Social Good is about storytelling. Data on its own is just numbers, but when visualised effectively, it becomes a story that can inspire action. This transformation—from raw data to compelling narrative—is the heart of our work and the reason behind the tangible impacts we’ve been able to achieve.

One of the most striking examples of this was a project for an organization engaging youth at risk through informal music mentoring, Noise Solution. 

Noise Solution’s unique approach combines music, mentoring, and a commitment to transparency, using data to showcase their impact. Volunteers from Viz for Social Good stepped in and created a series of visualisations that highlighted key insights. Gena Falzon, one of the volunteers created a sophisticated data interrogation tool designed to explore participant well-being changes across various demographics.

Reflecting on his experience about the project with VFSG, Simon Glenister, CEO/Founder of Noise Solution, shared:

“Pro bono work can often be quite hit or miss—sometimes it’s great, sometimes it’s not.  I can genuinely say hand on heart that this has been amazing. We are absolutely delighted with the work that’s been done.” 

Another volunteer, Shazeera Zawawi, brought a unique twist to data visualisation with her data sonification approach, turning data into musical notes that reflect the well-being journey of Noise Solution’s participants. This creative and playful approach aligned perfectly with the organization’s musical roots. You can listen to Shazeera’s innovative work on her data sonification dashboard here.

Beyond the dashboard: long-term impact

While individual visualisations are impactful, the long-term goal of Viz for Social Good is to build capacity within non-profits. We want them to see data not just as a reporting tool but as a strategic asset—a tool that shapes their decisions, amplifies their stories, and strengthens their ability to drive change.

Embedding data visualisation in daily operations

One of our key aspirations is to help non-profits integrate data visualisation into their day-to-day activities. While our current projects provide immense value during their timeframes, the reality is that many non-profits are left on their own after the project ends. This time-bound nature of our engagements, spanning just four weeks, can sometimes feel like a missed opportunity for deeper, sustained impact.

We envision a future where data visualisation becomes an integral part of a non-profit’s culture and operations—not just something done for special reports or campaigns, but a foundational practice that informs their strategies, engages stakeholders, and tracks progress continuously.

Building and leading a global community

The volunteer spirit

While our mission focuses on supporting charities, an unexpected but rewarding outcome has been the positive impact on our volunteers. Many have experienced career transformations, gained recognition within their organizations, showcased their work on their CVs, and honed their presentation skills, among many other benefits!

One such volunteer is Satoshi, a data professional from Japan. Satoshi discovered Viz for Social Good during a time when he was searching for a way to use his skills for meaningful work. His first project in 2018 involved creating visualisations for a non-profit Dear Tech People, dedicated to unearthing the data behind diversity in tech. Satoshi has now participated 33 times!

Lessons in leadership

When I first took on the role of Director Operations, I thought the “job” (not paid)  would be straightforward: match volunteers with non-profits, oversee projects, and celebrate the outcomes. What I didn’t anticipate was the complexity—and the profound rewards—of leading a global community.

One significant challenge has been finding great charities that not only have data available but are also ready to leverage it effectively.

Another ongoing challenge is the constant search for funding partners to support our initiatives and ensure the sustainability of our work.

One of the unique aspects of my journey with Viz for Social Good is that, while I am a leader, I also deeply enjoy participating as a volunteer. Since my first submission in March 2020 for Sunny Street—a project close to my heart—I’ve contributed to a total of 18 projects. These experiences have been some of the most rewarding parts of my involvement, allowing me to connect directly with the work our non-profits are doing, support their missions, and grow as a data practitioner.

Personal growth and the future

What Viz for Social Good taught me

Leading Viz for Social Good has been one of the most transformative experiences of my life. It’s taught me lessons that extend far beyond the realm of data and non-profits, shaping who I am as a person, a leader, and a professional. Each step of this journey has been an opportunity to learn—not just about how to manage a global initiative but also about the deeper values that underpin meaningful work.

One of the most profound lessons I’ve learned is the power of community. Viz for Social Good isn’t just a network of volunteers; it’s a family. Over the years, I’ve had the privilege of building lifelong friendships with people from around the world, united by our shared passion for social impact. These relationships have been a source of inspiration and strength, especially during challenging times. This was particularly evident during the 2023 Viz for Social Good Summit, when my fellow board members surprised me with a special recognition. As volunteers who dedicate countless hours each week to leading this initiative, this gesture deeply touched me—it reinforced that we’re not just colleagues working toward a common goal, but a community that truly values and supports one another.

Perhaps the most surprising lesson has been the sheer joy of giving. When I first joined Viz for Social Good, my motivation was to help others. I wanted to contribute my skills to causes that mattered and to support non-profits in making a difference. What I didn’t anticipate was how much I would gain in return. The act of giving—whether it’s time, skills, or encouragement—has brought me immense fulfillment. It’s taught me that the greatest rewards often come not from personal achievements but from lifting others up and witnessing their growth.

As I reflect on this journey, I am filled with gratitude for everyone who has been a part of it—the volunteers, non-profits, supporters, and mentors who have made Viz for Social Good what it is today. It’s a reminder that we are all capable of creating change, and that sometimes, the most profound transformations come not from what we give but from what we learn along the way.

The road ahead

As I look to the future, I’m filled with excitement for what’s possible. Viz for Social Good has already achieved so much, but I believe we are just scratching the surface of what’s achievable. 

The importance of strategic sponsorship 

To continue our work and expand our impact, we must also address the critical challenge of sustainable funding. Non-profit and social good initiatives like Viz for Social Good rely on strategic sponsorships to cover basic operational costs. We are actively seeking partnerships with corporations, technology companies, and philanthropic organizations that align with our mission of using data visualisation for social change. These sponsors aren’t just financial supporters; they’re collaborative partners who share our vision of leveraging data to drive meaningful social impact. By securing consistent funding, we can invest in our infrastructure, support our volunteer network, develop more robust training programs, and extend our reach to underserved communities. Our sponsorship strategy will focus on building long-term relationships with organizations that see the transformative potential of data visualisation in addressing global challenges.

Finally, I’m excited to explore new ways to use technology for social good. The rapid pace of technological innovation presents both challenges and opportunities. Emerging tools like artificial intelligence, machine learning, and immersive analytics have the potential to revolutionize how we approach data visualisation. AI-driven insights, for example, could help us uncover patterns in complex datasets that might otherwise go unnoticed, while immersive technologies like augmented reality could make data stories more engaging and accessible to diverse audiences.

Finally 

The road ahead is filled with opportunities to deepen our impact. Emerging technologies, expanding global networks, and the growing recognition of data’s value create new possibilities for Viz for Social Good. But at the heart of it all remains the same belief: when people come together with a shared purpose, they can create extraordinary change.

The journey is only starting. Every dataset holds a story waiting to be told, and every story has the potential to inspire action. Let’s continue to turn data into impact, one visualisation at a time.

How to join Viz for Social Good 

Joining our community is simple, free, and open to anyone passionate about using data for social impact.

Getting started

1. Visit www.vizforsocialgood.com to explore projects and resources.
2. Sign up as a volunteer to receive notifications about new projects.
3. Connect with a Viz for Social Good lead for support and guidance.

Ways to contribute

• Refer non-profits: Know an organization that could benefit from data visualisation? Refer them to us.
• Become an ambassador: Represent Viz for Social Good globally, inspire volunteers, and advocate for data-driven change.
• Join the committee: Help shape the organization’s future and expand our reach.

Spread the word Share our mission at data conferences, user groups, and among potential donors. Your voice can inspire new volunteers and supporters.

The post Harnessing Data for a Better World appeared first on Nightingale.

You’ve surely seen this map or a variation of it somewhere on the internet. If, like me, you’re a professional information designer, you know it’s part of an entire genre, including animated versions of it:

Or the effect of distortion on simple concepts like Great Circle Distance. This is the area within 5000km of Paris on a Mercator projection emphasizing the geographic distortion that happens the more north you get. You would expect a circle but instead get a shape reminiscent of a guitar pick.

Or interactive versions like this one by Ian Johnson that let you examine how different projections distort the shape and position of countries in different ways.

If, on the other hand, you have no idea what’s going on here, the short answer is that in transferring the shape of the surface of the Earth from a sphere to a flat screen you have to make certain decisions about whether and how much you want to maintain accurate representations of distance, area, grid alignment and other features. This is an editorial decision, there is no one right way of showing geography. Like most problematic subjects, Aaron Sorkin’s surrealist comedy The West Wing provided an overly idealistic and peppy explanation of the problem of map projections.

These maps are popular because they touch on the contingent nature of information display and emphasize the importance of revealing the human hand in communicating information. Sadly, we’re so constrained by our limited data visualization literacy that we can only really discuss these design choices with the venerable choropleth map because everyone learned how to read it in their 7th grade geography course. Every time one of these maps shows up on Reddit or BlueSky it is met with much excitement and adulation. Well-meaning people have claimed that the Mercator projection has made us think that northern hemisphere countries are better than equatorial and southern hemisphere countries because their size is exaggerated. All of that is a real problem because the “amazing” quality of these maps is shared by another amazing map.

Land doesn’t vote, the saying goes. And even when administrative regions (whether practically empty states in the American West or tiny countries in the EU) exert an outsized influence that’s not a reference to their geographic area but rather to their population. There are times when you need an accurate understanding of the area of land. You might be a farmer looking to expand your acreage or planning a new Amazon warehouse, but it has almost nothing to do with politics, culture or society. When we celebrate maps that make land important we are unintentionally enabling much more problematic maps like the Impeach This map.

There is a difference between maps created to show the distortions inherent in map projections and those created intentionally to mislead. But for readers it’s not so clear when we lack the tools to talk about them meaningfully. The distortions of the Mercator projection don’t just exist in some theoretical vacuum—they’re a reminder of how easily visualizations can be weaponized to amplify or bury ideas. To counteract this, we need to get better at asking specific questions: Why this projection? Why these data choices? What’s being highlighted or hidden? By learning to spot and articulate these design decisions, we can go beyond cheering for “amazing” maps and start seeing when they’re actively doing harm. Until we learn to name the ways maps shape narratives and teach others to do the same, we’ll stay stuck in a cycle of celebrating the familiar while missing the bigger picture.

Back in 2015, I attended the NACIS conference in Fargo, North Dakota. Fargo was selected because it was nearly at the center of North America but, as anyone who was forced to fly there on a series of progressively smaller airplanes knows, it’s actually much farther away from the rest of North America than, say, Chicago. That’s because in the modern world “close” has little to do with area and more to do with the transportation networks we’re embedded in via road, rail, sea lanes and air routes. The way we refer to sea and air travel (routes and lanes) is a reminder that even the crow doesn’t fly in a straight line.

But to talk about the networks on which we move goods, people, electricity and information would require people to know something about networks. Unfortunately, we don’t learn about networks in the 7th grade like we do maps. And it doesn’t stop there, if you ask a data visualization expert they’ll often tell you that you, too, do not need to learn how to read a network visualization or a flow diagram or any other kind of complex data visualization. Which is unfortunate because networks better explain the modern world than most amazing maps.

As a result, we’re stuck with only a few familiar methods for displaying anything more complex than a bar chart or line chart. Maps are one of those few methods that we know people can read. That familiarity gives us the ability to then have a second-order conversation about the data transformation and other information design decisions we have to make when it comes to any kind of map or chart or diagram. And that’s an interesting point to make, that’s why those maps are so popular. But there are so many other interesting points to make about data visualization, and we need to spend more time popularizing those rather than just repeating this same fact about map projections. If we don’t move on to other interesting points about information design, then we’ll never grow as readers or practitioners.

The post This Map Helps Fascists appeared first on Nightingale.

I can conjure up another pleasant daydream: I imagine two family members on opposite sides of the political spectrum looking at data visualizations on a host of issues—poverty, educational attainment, immigration trends. Then, they each speak from the heart and consider what can be done to address issues exposed through the data. It’s hard work, but they begin to understand each other’s perspective and see a middle path.

I know these sound like flights of fancy. After all, who among us actually uses public data to decide how to vote? Very few of us, I’m sure. I can’t help but wonder, however, if we could all benefit by nurturing this capacity in us. 

Why try to rebuild political dialogue through data? 

These days, it seems, we crave more and more data in so many facets of our lives, yet we disregard its power in political discourse by not seeking out unbiased facts on issues or community well being. Data, for example, is central to a well-functioning business, with real-time indicators and sophisticated dashboards regularly leveraged to plot next steps. Many of us, too, now measure our sleep, steps, and daily exercise. We view the results as progress graphs on our phones and watches and take action accordingly. And in my realm of work, where I run a data storytelling consultancy to serve organizations working to improve the social good, I see that universities, government agencies, and foundations increasingly want to learn how they can better leverage and communicate data to champion their work. 

I don’t mean to suggest we willingly keep data at arms’ length in political discourse. Many of us, I’m sure, would invite data into our lives to help us perform civic duties like voting. It’s just that we’re collectively overwhelmed by the volume of unhelpful political messaging that permeates our minds—social media feeds that influence our thinking; political advertising that’s often built to make us feel tense; and rhetoric from candidates that may exaggerate actual conditions. It’s not easy for the everyday voter to think about available data as a tool for education when there’s already a cacophony of seemingly urgent messages drowning out everything else.

Despite these challenges, we should actively try to insert data into discourse, because of the power of data to bridge divides and help us get beyond angry rhetoric. The common post-election refrain I keep hearing from friends, family, and colleagues is that they just can’t fathom why someone voted for the person in the other party. Yet the election results are pretty clear that votes were roughly evenly split between the two presidential candidates, which means that many of us simply don’t understand the perspective of the other half. How could we when we’re battered by political messaging that may only confirm our own political biases? 

Relaying data about issues impacting our everyday lives, in essence, would allow us to find common ground and help us read from the same script—and such data would counter the foreboding images of doom in political discourse and social media feeds that taint our understanding of the world.

How can data help combat polarization and unhelpful rhetoric?

The question of why to do this work to leverage data to inform the electorate is easier to answer than the how to do this. After all, the media channels noted above—political advertising, social media content, candidate rhetoric—are strong forces that can’t easily be tamed. However, there’s reason for hope if we start by lifting up data about, in particular, local community. You see, local work offers the most potential to bridge divides. CivicPulse, in newly published research with the Carnegie Corporation, found that local government leaders say that polarization is not likely to impact their work. The smaller the community, in fact, the less polarization plays a pivotal role, according to their survey. 

Our local communities can indeed be safe harbors from polarization and a means by which we stitch back together a sense of mutual understanding. Local community may well have played a more vital role in understanding our world, say, 100 years ago. These days, our outlook is much broader as our horizons have expanded. Yes, we’re more worldly, which is good, but we’re also more susceptible to fear-inducing stories we hear happening in other regions of the country. 

The evolution away from community as a way to understand our world perhaps began to take hold as national media, both TV and newspapers, started to drown out local reporting. Then came the Internet, which further erased geographic distance, and, along with cable news, enabled us to focus only on digesting media related to our interests. And finally came social media, which allowed people to find common outlets for their anger in places distant from home. As a result, that sense of local community that helped keep us sane and less polarized lost its influence. 

Transforming wide-ranging local facts into community education

On the positive side, however, we’ve also become more adept over the last 20 years at finding, publishing, and visualizing data about our communities. I began working with community-level data in 2002, when I helped launch a data website, kidsdata.org, to raise awareness about children’s issues in California. Back then, it felt like me and my colleagues were on the cutting edge by visualizing and communicating data about local child well being. Since those early days, a range of web tools providing community-level data have proliferated. I keep a catalog on my company’s website listing resources with data for California communities, and there are now about 75 such data tools in this catalog.

Many of these sites—such as County Health Rankings & Roadmaps, the CDC’s Places website and AARP’s Livability Index—even have local data for essentially every community in the United States. In short, the data is there for us; we just need to mobilize it.

I know from the work I do with my clients that they’re eager to harness data about community and transform facts into action. My clients’ aims may be focused on encouraging local business leaders to join a collaborative; providing maps and graphs to elected officials to persuade them to pass a policy; or visualizing data about community conditions in order to obtain funding. 

Those are all worthy goals, but we also need to see local data as an asset to help educate the electorate. Twenty years ago, this wasn’t an option, given the lack of data, but local data is readily available, visualized beautifully through tools that are thoughtfully crafted to help shape our understanding of our communities. Granted, these indicators are not available in one place (and probably never will be). That’s a solvable problem; we can guide people to where to go for data on specific topics. And in all likelihood, the everyday voter may need some hand-holding, too—some kind of curation of the data, perhaps through stories that can add needed color. Here, too, there are workable solutions.

There are numerous other obstacles in our path. Funding for government data sources that fuel these data websites could dry up. Facts could be manipulated by domestic (or even foreign) agents. And some of us may become (or may already be) skeptical of data from public sources. Those are indeed obstacles, but they’re not reasons to give up on this endeavor.

What I’m actually most concerned about is the lack of collective action. It’s striking to me that all of these organizations which build their various data tools work on their own islands, and don’t typically join forces. We need to change that mindset to have any chance of combating the power of advertising and social media in political discourse. 

I, for one, am keenly interested in helping to corral together the vast data resources we’ve assembled about our local communities, then finding ways to marshal these data for the benefit of political discourse. My hope is that, if we can establish a beachhead by using data to inform our understanding of place and local community, we can slowly begin to provide people with clear-headed ways to understand their world—and each other.

Who’s interested in joining this journey? And who knows of similar initiatives focused on leveraging data to support discourse? I’d love to hear from you at andy@hillcrestadvisory.com.

The post Can Community Data Help Heal Public Discourse? appeared first on Nightingale.