The 4th Wave: Democratization

This article is a continuation of A Changing World

Speaking a shared language

Data visualizations are themselves accessibility tools to see patterns and make comparisons, sometimes across data sources with millions or more rows and countless fields. Even in seemingly simple data tables, encoding numbers with size, position, color, or other components can quickly help us see which values are larger or smaller, or pick out an outlier without having to read each individual number.

When we describe data visualization, we often talk in the language of charts. This taxonomic approach to identifying what kind of chart is suited to what type of analysis, communication, or exploration is useful for explaining how to read or create simple charts, like bar charts, line graphs, dot plots, and pie charts.  

But as we saw in the second wave of the field, the emergence of systems and shared language is powerful, and can allow us to think more about what is communicated through position, shape, size, and color.

To start, we need to more explicitly integrate these two approaches—–whole chart and Grammar of Graphics style atomic encoding—–in a way that acknowledges the tension between these two approaches but also how they can be combined.  An excellent discussion along these lines comes with Cogley and Setlur (Functional Aesthetics, 2022) emphasizing the importance of combining perception, semantics, and intent to make visualizations not only functional but also accessible, ensuring they communicate insights effectively across diverse audiences.

Increasing our fluency in the language of data includes learning about how Gestalt principles (how we interpret groups of information) and preattentive attributes are used in visualization design, both in individual charts and larger deliverables like dashboards and visual data stories. Then, exploring more about the nuances in our design decisions with color theory, typography, and the integration of UX design best practices as we continue to craft more interactive data experiences.

Terms like ‘preattentive’ and ‘Gestalt’ might feel like jargon that is counter to the idea that today’s fourth wave is about the ways data visualization is no longer niche and becoming more democratized. But breaking down charts into their components gives us a way to connect how we represent data in graphics with the ways information is visually represented in the world—which is critical for embracing non-traditional forms.

We can talk about encodings through the simple examples of objects we encounter daily. The color and position of the red, yellow, and green lights on stoplights, for example, tell us when to stop, go, or proceed with caution. The consistent position of each of the light colors, with red on the top spot, addresses the accessibility challenge for those who are colorblind—something we also consider in data visualization design.

For parents and caregivers, we stack and sort objects with our kids. Take a pile of LEGO, sort the pieces by size and color, and then create stacks of each. Line them up along an aligned scale, and you have a physical bar chart of LEGO bricks with color and size (length) representing different information.

For smartwatch users, like Apple watches, we hear people referring to “closing their rings,” referencing a set of three colored lines with each color representing a different metric from the day. As the metric count grows over the day, the line gets longer and curves into a circle. The display is certainly a data visualization, but we don’t need to assign a chart name to it for the visual information to be useful—you can see which rings were unfinished at the end of the day.

As people are exposed to increasingly creative ways of representing data, our role as data visualization creators expands. When we understand the languages of charts—whether it’s through Gestalt, Grammar of Graphics, or preattentive attributes—we stop treating a single chart as an isolated bit of communication and venture into a more complex understanding of the world where graphics can represent the systems we’re embedded in.

Embracing non-traditional forms

But as the role of the data visualization practitioner grows, we also see the continued democratization of data visualization both in how visualizations are created and their wide use to communicate not just facts but sentiment.

Through the first three waves of data visualization, the tech stacks available to create charts evolved, becoming ever-less reliant on coding capabilities to make increasingly complex graphics and data stories. Excel, as the most common visualization tool in the world thanks to its post in the Microsoft Office suite on nearly every computer in the world, has expanded the design capabilities and improved some of the default formatting, while the use of PowerBI for more complex dashboarding grows in adoption and recently added GenAI capabilities with Copilot. Flourish, Datawrapper, and Tableau make it possible for non-coders to create engaging, interactive graphics and scrollytelling features. 

These tools are exceptional in democratizing the creation of complex visual stories, but still rely on a certain access to and comfort with technology and working with data tables as a medium. GenAI tools like ChatGPT and Claude perhaps even challenge that requirement, with ever expanding analytical and charting capabilities, generating charts with a click of a prompt.

In contrast with ever increasing automation and GenAI enablement, we have seen an increasing recognition of the value of crafting graphics in physical space, with found objects, and through means that are increasingly analog and artistic rather than fully reliant on digital tools.

The Data Humanism Manifesto from Giorgia Luipi and her “Dear Data” project with Stefanie Posavec, now codified in the Museum of Modern Art’s permanent collection, give permission to use small, imperfect data as our medium. Mona Chalabi’s work is widely applauded, including with a Pulitzer Prize, for its recognizable hand-illustrated style and the visual metaphors she incorporates to make her messages sticky. 

Creating hand drawn charts or visualizations with objects isn’t new though. The practice dates back thousands of years to ancient civilizations and items like Incan khipus with their colored, knotted cords heavy with meaning. Part of recognizing the value in a more diverse range of visualization types also requires us to reflect on the history of this work in new ways and recognize a wider range of creators and innovators beyond the traditional canon of William Playfair, Edward Tufte, Stephen Few, and others. 

In this way, good data visualization should challenge the modern biases that influence how we communicate information. Much of our conceptualization of what makes ‘good’ visualization through the first three waves of modern data visualization is rooted in principles of simplicity; a read through the r/dataisbeautiful feed shows just how contentious the assessments of ‘quality’ can be when serving up a chart for tribute. ‘Good’ has often been measured, even in academic studies, focusing on precision (can you identify the insight) rather than expressiveness.

There isn’t one right way to communicate a dataset, and how you measure success should depend on your goals. Focusing instead on more creative forms that often involve communicating data within communities opens up entirely new opportunities for creativity and bridging the data literacy gap. 

We can celebrate the approaches underrepresented groups take in displaying information, from encoding information in textiles to data theater which shifts beyond visualization and pushes us towards new ways to experience data. This includes forms that rely less on shared language and leverage novelty (like the bar chart race) and placement in the communities where they can encounter data off of a screen (like community data murals like the 2022 Information is Beautiful Awards Unusual Gold winner from the Social Justice Center in Kenya).

Generative AI may revolutionize the ease with which we produce essays, images, and even videos, but creating truly impactful charts remains a more elusive challenge. Visualizations require a balance of clarity, context, and insight that is difficult to replicate algorithmically.

The broader democratization of chart-making is both a promise and a challenge: while more people can create visualizations, understanding and interpreting them demands a deeper fluency in data literacy. In this era of overwhelming visual information, our collective task is not just to make charts easier to create but to make them tools for richer, more meaningful communication.

Engendering Trust

Data is not objective, despite its outward appearance. But data can help us understand the world a bit more fully, and visualizations have the advantage of traceability. A good chart will give you details about the data source that enable you to trust but verify when something looks too good (or too terrible) to be true.

Instead of pointing to objectivity, we acknowledge that data visualization does have a fundamental truthfulness; “a truthful art” as Alberto Cairo says. But speaking the language of data visualization and having some foundational knowledge about how charts are created helps us assess if a chart is misleading or not. 

Data visualization experts can act as chart navigators, helping others develop their own framework for reading (and assessing the truthiness of) charts. We can and must do this without being condescending. The democratization of data visualization means our collective communities and society benefit from having more critical chart readers in the world, but only if we are considerate in how and why we give feedback.  

But, as creators, we cannot be so considerate that we avoid engagement with other creators. It might be uncomfortable for both those giving feedback and those receiving it but that discomfort does not compare to the real damage done when readers lack the context and support to read and evaluate the validity of data visualization products that are more and more being used to communicate with them about their world. Creators should expect and be prepared for feedback from fellow practitioners during the creation process or after the fact from readers (which might also include fellow practitioners).

Communities, like the Data Visualization Society, can foster inclusive spaces for learning and critique among peers. Dedicated challenges and community initiatives, like Makeover Monday, Tidy Tuesday, and Back to Viz Basics, are experiences designed to create feedback loops for the sake of learning. Peer feedback has the benefit of learning from others with expertise in visualization design, which may come with more specific recommendations rooted in research or design principles. 

Feedback that comes in public spaces, where readers share what they liked or disliked about a graphic, often occurs without context or knowledge of the constraints the designer faced. Public discussions provide the benefit of exposing design decisions and rich learning opportunities to bystanders on social media or wherever else the critique takes place. But tone and lack of civility can cause early career practitioners to pause before publishing.

Charts aren’t just made by designers anymore, making a critical eye even more necessary. We’ve seen how image generation with tools like Midjourney, DALL-E, and genAI tools with multimedia capabilities can make anyone feel like they’re an artist. This type of software has certainly found its place in the modern working world, despite the ethical concerns around fair use of various images and original artwork in the training datasets.

We are still in the era of too many fingers when it comes to genAI and data visualization. Ask DALL-E or Midjourney to make a chart and you will see nonsense. Ask an LLM that can run code and you’ll see simple charts based on the millions of examples of simple charts that we’ve made during our time on the Internet. This won’t last. AI-originated charts will undoubtedly become more sleek and difficult if not impossible to identify as different from human-created charts.

But charts have an advantage over art, video, and narrative text when it comes to evaluation: there are knowable rules for what makes a good chart and these rules can be taught. Perhaps this possibility of validation makes communication with data visualization more resilient in an age of AI. Charts can lie and are even designed by people to do so deliberately in some cases but you can spot these lies because they have to be visible in a way you cannot spot lies in a well-written essay or a video purporting to be reality.

Embracing complexity

Our role as data visualization designers is to increase the scope and scale of information retrieval, pattern recognition, and discovery of new information. In the best of cases, we make that process engaging, interesting, and even inspiring.  Knowing how to make a bar or line chart means you can enable optimization of decisions that require numerically precise comparison. Knowing how to make a more complex diagram like a network chart, allows you to enable your users’ discovery and action on patterns that only exist topologically.

“As a data visualization professional, you don’t need to know all these weird chart types deeply” is no longer good advice. In an AI-enabled world, many more people will be able to create basic charts with ease. This is a fundamentally good shift, which we’ve already seen shape our field with the advent of tools like Datawrapper, Flourish, Canva, and more.

One of the fourth wave value propositions for data visualization designers is making the complex understandable.

Not through over simplifying graphics and reducing complex data stories to single slides, but by thinking about how we remix ideas and encodings in creative yet understandable ways leveraging formats like scrollytelling, presentations centered on data stories, and more.

During earlier waves of data visualization, complexity was often seen as a barrier: complex graphics were harder to produce and more challenging to interpret. Practical advice for busy dashboard developers was to avoid complexity and focus on tried and true charts that everyone knew (bar charts, line charts, and maps being the most common). Plus, the tools and skill sets required to create detailed network diagrams, dynamic simulations, or advanced statistical visualizations were often limited to experts who commanded both the technical and conceptual domains.

Today, the strategic value of data visualization lies increasingly in the capacity to represent and explore complexity. Complexity does not simply mean inventing more intricate chart types or endlessly layering on more variables. Rather, it involves designing visualizations that help audiences navigate nuanced, interconnected systems—whether it’s understanding the global supply chain behind everyday products, the intricate web of relationships in a social network, or the probabilistic models that underpin climate projections—in ways audiences find insightful and, in the best of cases, delightful to read.

Embracing complexity in the fourth wave involves guiding viewers through layered stories and systems rather than forced filtering to a single, simplified narrative. Today, data visualization creators can integrate multiple perspectives—quantitative, qualitative, topological, temporal, spatial—to help people see beyond immediate headlines or single metrics. Instead of flattening a story into one bar chart, creators might design interactives that let readers pivot between views, highlight anomalies, and drill down into details. Or they might craft scrollytelling experiences that gradually reveal relationships among variables, using animation and annotation to scaffold comprehension step by step.

Modern tools increasingly support this approach. Interactive platforms enable readers to explore complex data at their own pace, revealing insights only when the user is ready. Advanced techniques, such as small multiples, linked brushing, and coordinated visual filters, provide multiple points of entry into a data story.

In the fourth wave, complexity is no longer something to be minimized at all costs.

It becomes a hallmark of our evolving craft: the ability to represent our multifaceted reality in ways that different audiences can engage with critically and productively. As chart creation tools become ubiquitous, the role of the skilled designer evolves into that of a thoughtful guide—someone who helps others move from simple, one-dimensional charts toward richer, more meaningful representations of our world.

Contextual literacy and ethics

As complexity becomes more accepted—indeed, expected—data visualization practitioners must champion data literacy. Complexity should not be a veil that obscures meaning; instead, it should be a scaffold that elevates understanding. We must teach readers to recognize when a chart is hiding something: Are we seeing averages that mask key disparities? Are relationships presented without historical context or underlying sample sizes? Embracing complexity means giving audiences the tools to question and interpret what they see. This isn’t about replacing simplicity with confusion; it’s about respecting the viewer’s capacity for nuanced understanding and guiding them through that experience.

It’s also about building an audience that can appreciate and support well-designed information. Because it’s not enough to have great information designers, we need great information readers who support and promote them in a virtuous cycle.

At the same time, we must remain vigilant about ethical considerations. Complexity can be used to mislead, to bury questionable assumptions in a tangle of nodes and edges. As practitioners, our ethical mandate is to highlight uncertainty, reveal data lineage, and ensure audiences have access to underlying sources. Complexity, when handled ethically, isn’t a pathway to obfuscation—it’s a route to deeper insight.

This demands a refocusing on transparency around our design decisions: as creators, we must show how data is aggregated, which assumptions were made, and where uncertainty lies. This openness fosters trust and encourages a more critical engagement with charts. A network visualization might include a side panel detailing how nodes and edges were defined, or a climate model might include error bars and annotations that highlight uncertainties inherent in predictive modeling.

The role of community in the fourth wave

As data viz practitioners, the further democratization of data visualization demands we broaden our scope from focusing on creating charts and instead dedicate time to understanding and giving greater consideration to how people read data visualizations. In the 2024 State of the Data Viz Industry Survey, the most pressing challenge reported by data visualizers was the lack of data visualization literacy.

Expanding access to the craft of data visualization design makes building communities of practitioners more critical than ever.  Together, we will share new ways of working, grapple with emerging ethical questions around the use and misuse of AI tools, and celebrate the creativity of our field.

But as we look inward as a discipline, we also need to look outward. What role can we play as data visualization practitioners to ensure people are not left behind in engaging with the world, understanding charts in the news, or even advancing in their work because of gaps in data fluency? Reading charts is a learnable skill, as is creating them. 

Organizations like the Data Visualization Society have a responsibility to create shared spaces for learning the shared language of charts, but some of the most effective teaching moments are likely to come not from big Zoom calls but instead through one on one interactions where we help people in our lives make sense of information, or call attention to a misleading chart going viral.

We know from research that simply presenting something in a chart or with a formula gives the impression of objectivity and believability. But readers need to finally, deeply learn the truth that data visualization practitioners have long known: tables are not an unbiased view into “raw data” but rather are their own situated encoding that preferences particular views and variables.

In this fourth wave, we hope everyone can see that this wave isn’t only lapping on the shores of the data viz world—it’s crashing into our everyday lives, decisions, elections, and more.

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A PDF version of this white paper is available here.