Like most of what surrounds us, data is a human-made fiction. Which means that it, and whatever we understand it to be, has been made up by us (and by us I mean humans). So whether you understand data to be neutral or truthful or biased or reliable, it is regardless made up by us. But— we still live in the world and produce data and gather it and make things with it. Things like, of course, data visualization.

As professionals working in data visualization, we often take data for granted. And it could be argued that the general public has come to think of data and its visualizations as something neutral and indisputable. But the data, the designer, and even the readers can and will influence the understanding and nuance of a piece. This article outlines and analyzes the elements that influence the production and consumption of data visualization — which, rather than objective, is very subjective.

The data

Data visualization is often described as incorporating elements from many disciplines: map design principles from cartography, conventions of displaying numerical data from statistics, best practices for the use of type, layout and color from graphic design, and principles of writing and storytelling from journalism. While this allows for a lot of creative freedom, it is also a double-edged sword: it imbues data visualization with certain predetermined expectations. Predominantly, these are the common perceptions that data visualization is ‘impartial’ and portrays ‘the truth’. It’s often thought that the data being used is neutral, unbiased, and objective, and that in visually mapping, rendering and displaying information the graphic devices used do not obscure the content they display.

The problematic assumption that data is neutral comes from it often described as being in a “raw” state that represents collected phenomena with a disembodied nature from its human agent. Data thinking is thought to be pre-existing ‘out there’, ready to be harvested and represented as transparently as possible. It is a term that has come to invoke ideas of a resource to be exploited or a material to be manipulated (so much so, that we even come up with ridiculous terms such as “data mining”).

A few illustrations that come up from a quick Google search of ‘data mining’

But the increasing relevance of data science and data visualization has also prompted the interrogation of what we even understand data itself to be, as well as how we formulate claims of truth. And, borrowing Lisa Gitelman’s expression, "there is no such thing as raw data: it’s an oxymoron¹". Data is anything but raw, and we shouldn’t think of data as a natural resource but as a cultural one that needs to be generated, protected, and interpreted. Data and its collection, manipulation and presentation both empowers and disempowers the people and the interests that are represented (or misrepresented) by the data.

The designer

Turning data into structured information means giving shape to the data. By designing visualizations, the designer is an agent in the whole process, and, inevitably, another piece of data. Just as numbers and data don’t just exist, data visualizations don’t, either: they are created for specific reasons, with a purpose, and by someone. The role of the designer includes either all or part of the thinking, collecting, documenting, mapping, analyzing, reflecting, translating, synthesizing, and concluding around the data and the graphics.

Data visualization is often described as incorporating elements from many disciplines: map design principles from cartography, conventions of displaying numerical data from statistics, best practices for the use of type, layout and color from graphic design, and principles of writing and storytelling from journalism. And any graphic representation of information is phenomenologically distinct to the information itself.

From maps to numerical data to storytelling. Designs by CLEVER°FRANKE for the Africa Climate Mobility Initiative platform.

But it is rather the understanding of how this someone shapes the piece that is most relevant to take into account. Whether they are the generators or facilitators of content, it is precisely how the designer’s view of the world shapes their designs that can, in my view, be claimed as an element influencing the design of data. As humans, we are intimately and physically connected to the work we produce, and it is inevitable that our work bears our stamp. Different visualizations are differently effective, are well or poorly designed, and every dataset can be visualized in different ways. Moreover, individual designs, as well as ideas about designing, are related to wider sets of values—at its best, graphic design is merely reflecting the sentiments that culture at large is already feeling. The designer is a communicator that has agency in the message they are translating into visual form.

Tasked with the exact same brief, designers respond with different outputs. Spreads from Page 1: Great Expectations, published by GraphicDesign&.

Despite our best efforts, what we do is never truly neutral, nor purely objective. And it shouldn’t be. We say that visualizations are successful when they communicate ‘what the data has to say’ with efficiency. But graphics do not transparently reveal data — really, they create a new interpretation of it. Perhaps the question is not one of truthfulness but of reliability, so that designers can reject the discourse of objectivity and instead actively adopt a reflexive position where they are able to understand the relationship between their own and others’ ideological assumptions in the acts of communication and meaning-making.

The reader

Lastly, it is important to place greater emphasis on the responsibility that the reader has as a consumer. Rather than demanding transparency on the side of the data visualizer, they should acknowledge that the choice to engage or not with a data visualization design is ultimately down to them, and that they must do so knowing that what they are consuming is not only the product of someone else’s mind, but also that of a broader discourse-at-large. Their challenge as readers is in understanding the multiplicity of methods that can comprise a design language. Data is everywhere: in every headline, and at the center of every conversation. Only a few months after the covid pandemic began, data became an essential language to understand and make sense of a rapidly changing world. But how fluent in this language is the general public? Just as we learn how to read, we need to learn how to read data visualizations: how to spot bad statistics, understand context; how uncertainty can and should be visualized, and how missing data is also part of the story.

The Dutch Corona Dashboard, designed by CLEVER°FRANKE, shows how the data we don’t have is as important as the data points we do have. To fully understand a phenomenon, the surrounding circumstances of a number must be treated with the same importance as the number itself.

Data visualization is increasingly a way in which we translate the information we produce, so it is crucial that we recognize how much of our lives is mediated and shaped by it. Seeing data, for example, is a group of research projects which aim to understand the place of data visualizations in society. It also publishes resources to help non-experts develop their ability to make sense of data visualizations. In a data-driven world, we need to learn how to critically engage with the graphic devices that surround us.

The amount of data we produce, collect, and process is only going to grow. Add to this purposely erroneous information produced by humans (read: fake news), as well as the inevitable proliferation of machine-produced content yet to come. The world we live in is increasingly shaped by datasets, algorithms, and pre-trained models. As well as ensuring we train critical data designers, it is imperative that as readers we become ever more critical and skeptical of the content we consume, and equipped to judge its veracity and credibility.

Closing thoughts: nothing changes if nothing changes

So what is our role as data designers? How do we avoid the catastrophe? For starters, we can see ourselves more as storytellers than as truth-revealers—as experts endowed with the power to create form, solve problems, pass judgment, and convey meaning. Data visualization is the result of knowledge translation, just as any other technology—a clock, a compass, an abacus, a map, a book—is.

Data visualization: just another technology

We need to use our expertise and knowledge of design, the power of data, and the available technologies to enable the exploration of (complex) information with intelligent, meaningful pieces. Below is a proposed framework for data designers:

1. Don’t trust data

By using data and representing it visually in our projects, we confirm its authenticity unless we explicitly undermine its validity. Always ask: where does the data come from? Who collected it, organized it, categorized it? When did they make it? Why did they make it? What social/cultural conditions was it made under? Just because we use data as our material doesn’t mean we should blindly believe in it.

2. Date the data

Enquire, be curious, spend some time with the data: it is where you pave the grounds, set the tone, understand the material you are working with. Gathering, understanding, and organizing the data is like shopping for paint and brushes. Things which seem quite simple like a data inventory are actually a key part of the process. Before we get on to designing, we need to fully understand what it is we are designing.
Remember: data visualization can be data analysis—charts let us explore and understand patterns in data using visual encodings, rather than through mathematical models.

3. Look around the data (and don’t be afraid of the missing data)

More often than not, the data lacks data (or we lack data completely). Be resourceful: a lot of information is available out there. Make connections, complement the data. Keep in mind that data visualization design prioritizes certainty, yet uncertainty is just as important to visualize as certainty. It often tells you more than that which is there. Design is more about the message you are transmitting with the data, than about the data itself.

4. Be transparent about the data and the design

Consider bias in data collection, inclusions/exclusions, gaps or omission in the data, provenance and data quality, and expose these. Be transparent about your sources and methodologies. The raw data is not always needed, but how you made sense of it and where it came from can expose the decisions that contribute to the design of the visualizations. One of the principles of the Data Feminism movement by Lauren Klein and Catherine D’Ignazio, Embrace Pluralism, proposes how self-disclosure and an embrace of pluralism can expose the decisions that contribute to the creation of data visualizations—in this way shifting from the current emphasis on objectivity in favor of designs that facilitate pathways to multiple truths.

5. Acknowledge your position

Data visualizations need to be situated in time and space to counter the visual rhetoric that connotes omniscience that suggests data has been gathered, and is being viewed, from nowhere — a position often confused with objectivity. Subjects who acknowledge the contingency of their own position in the world produce greater objectivity than those who claim to be neutral. Just as there is no thing such thing as raw data, there isn’t either a one-size-fits-all visualization.

Neither data, its visualization, nor its visualizer represent a single truth, but rather offer a constructed, fallible, and subjective view of the world. It is crucial that as readers we recognize how much of our lives are mediated and shaped by data visualization, and that this calls for greater comprehension of the graphic realm we constantly engage with. Visualization has a disruptive, revolutionary potential, and the agency of designers needs to be recognized as a force to shape the world they aim to represent. If as designers we can create visuals that encourage careful reading and personal engagement, people will find more and more real value in data and in what it represents.

Read more

Joanna Drucker. Graphesis: Visual Forms of Knowledge Production. Cambridge, Massachusetts: Harvard University Press, 2014.

Peter A. Hall and Patricio Dávila. Critical Visualization: Rethinking the Representation of Data. London: Bloomsbury Publishing, 2022.

References

1. Lisa Gitelman (ed.) “Raw Data” Is an Oxymoron. Cambridge, Massachusetts: The MIT Press, 2013.