Barriers To A Thriving Data Visualization Culture

Part 1 of a series on creating a culture where dataviz professionals thrive and unlock the power of your data.

David Mora
Aug 13 · 6 min read

There’s an incredible, untapped opportunity…

It’s no secret: the explosion of data continues to revolutionize organizations. Across the world, forward-thinking leaders embrace this opportunity, pioneering new ways for their organizations to utilize their data. As data floods in, a new kind of data expert is rising up, equipped with the rare skills to bridge the gap between complex information and the human insight and understanding needed to harness it: the Data Visualization Practitioner.

While data scientists give you the raw material to be “data-driven,” dataviz professionals provide the data vision: they make critical information useful to those who need it most. The tools they create don’t just give insight into the data, they provide sharable windows around which the organization can discuss the data’s use, potential, and validity, driving cross-department collaboration and innovation.

Organizations that have integrated data visualization professionals effectively have already yielded tremendous benefits: they’ve given directors of critical public transportation systems the ability to spot and mitigate catastrophic overloads; they’ve created novel, life-saving methods for doctors; they’ve transformed the public’s understanding of global health.

But — despite these examples — dataviz practitioners, again and again, fail to realize their full potential for their organizations.

What’s going on?

The challenges to realizing a great dataviz culture

To understand the challenges, let’s envision a dataviz professional named Alejandra. She’s got mean Python chops, a knack for data exploration, and expertise in visual perception and human-centered design. She’s surrounded by brilliant leadership in a data-driven org. Yet she often struggles to bring her skills to fruition in her org, for three main reasons:

Illustration by Natalia Kiseleva (eolay13)

1) Dataviz professionals struggle to effectively engage organizational leadership

In many organizations, unlocking the power of dataviz practitioners rests largely on leadership. But leadership — pressed to deliver products — typically has little space to explore new processes and methods. This limits their ability to establish a robust dataviz culture, dataviz professional Bridget Cogley points out.

Alejandra’s managers often come to her with a chart request that will get them by on their tight deadline, rather than collaborating to create powerful and nuanced data visualizations.

Needing charts that feel familiar and trustworthy, leadership often must respond to the introduction of new visualization tools by asking, “Can you use charts I’m familiar with, like the kind they have in Excel?”

As a result, leadership leaves Alejandra has little room to apply her expertise, ultimately costing the organization the opportunity to understand and use its data more effectively.

A woman sits in front of a computer with dataviz on it,  looking pensive. Her thought bubbles read: “why?” and “that’s why!”
A woman sits in front of a computer with dataviz on it,  looking pensive. Her thought bubbles read: “why?” and “that’s why!”
Illustration by Natalia Kiseleva (eolay13)

2) Dataviz professionals must articulate their value across the domains of tech and communication — each with vastly different tools and work-styles.

Think about Alejandra again: her dataviz skills bridge both tech and communication — an incredible opportunity for her org to integrate across domains. But also a huge challenge: she has to learn to communicate its role to wildly different stakeholders, both of which see half her work as lying in someone else’s domain.

The challenges for each domain are distinct:

Working with Communicators & Designers:

While her org has skilled communicators and designers, they already have a rich toolset of methods for communication. When it comes to dataviz, the need to leverage information design means the work seems to sit squarely within the domain of less practical approaches that designers tend to dismiss as overly theoretical and academic.

Further, designers often focus on trends in data visualization from thirty years ago (circa Edward Tufte) which emphasize simplicity and clarity — trends which perfectly complement modern UI/UX’s drive for frictionless interfaces amenable to users’ scarce attention. While dataviz professionals often use visualization to simplify, they also know when a given task necessitates more complex methods. Clarity is great, but making simplicity the principle aim of dataviz stifles the rich science, methods, and possibilities its practitioners bring to the table.

(It’s critical to acknowledge that defining the term “simplicity” — let alone what it’s role should be in dataviz or design— is beyond the scope of this article. For one perspective on how simplicity fits into dataviz’s larger development, see Elijah Meeks’ “3rd Wave Data Visualization.”)

Working with Data Scientists:

Data scientists don’t shy away from complexity. But in the world of data science, resources are focused on data preparation, not tools for understanding and communicating data. The reason is clear: data scientists already have a rich (if domain-specific) toolset for exploring and communicating data within their own field.

As dataviz professional Nicole Edmonds explains, the result is that “the usual focus is on the engineering aspects of data acquisition and cleansing, and the nice charts and graphs are an after-thought…” In other words, data visualization happens in the leftover time after the “real work” of creating and analyzing the dataset is done.

3) The term Data Visualization misleads: it focuses on output, not process

The very term data visualization sounds like an output (data in visual form), when in fact the most defining aspect is actually a process and mindset (data-grounded insight as an iterative collaboration).

Think about it. What does the term Data Science conjure up in your mind? An evidence-driven method that takes time and expertise; something which contains complexity and uncertainty that can be articulated and reasoned about. A science. What does the label Data Engineering suggest in your mind? Data Analysis? Data visualization, in contrast, is decidedly a noun.

You can’t output a “data science.” You can definitely output a “data visualization.”

Where to now?

As data floods in, the organizations who excel will not be the ones who amass the most data, but those who create cultures where that data can be understood, communicated, and ultimately transformed into insight.

What blocks teams from unlocking this thriving dataviz culture is clear:

  • Struggling to engage with resource-constrained leadership
  • The inherent challenge (and opportunity) of straddling both design, communication, and data science.
  • Focusing on dataviz as an output — not a collaborative process which produces insight by understanding stakeholder needs.

The question is: what can your org do to overcome these challenges?

In Part 2, we discuss how to harness the power of dataviz professionals in your org. Dataviz professionals should start small, with visualizations and concepts that their colleagues know. By gaining the support of one leader, and easing any anxiety their colleagues might have, other stakeholders also begin to also buy into a more collaborative process. Leadership in an organization must work together to make dataviz a process, not a product. This means allocating space to put people’s experiences and needs at the center of all decisions, enabling collaboration and feedback to hone the visualizations.

Smiling woman making a peace sign with speech text reading “See you”.
Smiling woman making a peace sign with speech text reading “See you”.

Thanks to the Data Visualization Society contributors to this discussion: Bridget Cogley, Keisha Carr, Melanie Mazanec, Charles Saulnier, Elijah Meeks, Jane Zhang, Nicole Edmonds, Tricia Aung, Brandon R Hecker, Andres Garcia, Wendy Small, William Angel, Amanda Makulec, Stephen Singer, Jason Forrest, Phil Hawkins, Erica Gunn.

Special thanks to Eleanor Collier, Elijah Meeks and Jason Forrest who helped craft and revise this article. And hats off to Natalia Kiseleva for the illustrations.


The Journal of the Data Visualization Society

David Mora

Written by

Data Vis Engineer/Designer at The Center on Rural Innovation.


The Journal of the Data Visualization Society

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