Illustration by Natalia Kiseleva (eolay13)

How To Create A Thriving Data Visualization Culture

David Mora
Aug 23 · 8 min read

This is part 2 of 2 in a series on creating a culture where the dataviz professionals can thrive and unlock the power of your data. The previous article provides context for why orgs often fail to harness dataviz’s full power.

There’s an incredible, untapped opportunity…

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.

As we covered in the last article, data visualization professionals bring tool sets honed to uncover those insights in the data. But many organizations struggle to effectively utilize dataviz, blocked by several critical challenges:

  • How do you engage with resource-constrained leadership to establish the processes needed for dataviz?
  • How do you promote the importance of dataviz, when it bridges design, communication, and data science?
  • How can you facilitate a collaborative process for dataviz that produces insight by understanding stakeholder needs, when many see it as a one-and-done output?

These are hard but critical questions — getting them wrong could cost your organization precious insights into its most important data.

That’s why this article brings you practical advice on how to overcome these challenges and create a thriving dataviz culture, synthesizing insights from a recent discussion in the Data Visualization Society (DVS) between dataviz experts across the world.

Creating Great Data Visualization Culture

Before you build a great dataviz culture, you have to envision it.

During the DVS conversation, we came to a strikingly concordant vision for what that looked like: dataviz culture at its best embraces a human-centered design process focused on the audience and their core needs, not just the visualization tools or forms. This culture doesn’t just make data useful — it fundamentally gives its org vision into how it uses data to begin with.

OK, but what does that look like in action?

Alejandra sits at her computer, thinking. A speech bubble reads: “Why?” and “That’s Why!”
Alejandra sits at her computer, thinking. A speech bubble reads: “Why?” and “That’s Why!”
Illustration by Natalia Kiseleva (eolay13)

To help, let’s envision a dataviz professional named Alejandra. Alejandra’s got mean Python chops, a knack for data exploration, and expertise in visual perception and human-centered design.

What about her organization’s dataviz culture enables Alejandra to flourish and bolster her organization’s strategic goals? For starters, Alejandra's teammates approach her with key questions and expect a collaborative process to answer them, not a predefined chart.

Example 1: Bridging domains

When a gap of understanding opens between the data science team’s machine learning results and the sales team’s ability to use them, the team looks to Alejandra. Presented with the open-ended challenge, Alejandra knows her value won’t come from pretty charts, but from the process by which she works with the sales team to understand the questions and challenges that define their work. That insight, in turn, powers visualizations that reveal the structures in the data that can clearly answer those driving questions.

This enables the sales team to make key discoveries that the data science team — often divorced from the nuances of sales — had overlooked. This helps the data team refine their own process and focus.

With space to direct a dataviz process centered on user needs, Alejandra’s work doesn’t just change the charts her org uses, she fundamentally changes their ability to communicate across domains and extract critical insights from the data.

Illustration by Natalia Kiseleva (eolay13)

Example 2: Helping data resonate with stakeholders

Alejandra’s work extends outside the org, too.

As her org teeters on the verge of losing their most important customer, Alejandra combines a keen sense of stakeholders’ needs with data storytelling to create a presentation highlighting the value her org produces for that key customer. Here, again, it’s not her technical knowledge or communication skills alone that make the difference. With the freedom to practice a human-centered design process and skills to analyze the data, Alejandra illuminates the data in a way that resonates with her target audience.

Alejandra’s work goes beyond charts — it guides and supports her organization by pinpointing what matters most to her stakeholders and providing tailored insight into how the data fits into that picture.

This is the ideal. But how do we get there?

The Solutions to Create Great Dataviz Culture

Great dataviz culture comes from three main approaches:

1. Build Trust and Relationships

You don’t need to change your entire organization — just meet people where they are. And be sure to engage the people on the extremes: the champions and the naysayers.

  • Start with what’s familiar: Begin with visualizations and data that stakeholders know and love. Not only does it show respect, it puts people at ease and in their element. Plus, it gives you a starting place from which to guide people forward.
  • Understand the client’s needs at a fundamental level: The beautiful challenge of dataviz isn’t just making chart choices, but listening deeply to stakeholders’ struggles and needs — understanding their toughest choices. This process reveals how you can support them. As dataviz professional Phil Hawkins articulated: **cough cough Design Thinking**
  • Get one leader on board: Hierarchy isn’t bad! …assuming you’ve got an advocate near the top. Dataviz professional Stephen Singer reminds us: “the larger the org, the more important it is to have an internal champion who is in leadership or otherwise influential to help keep things moving against inertia.” Winning leadership isn’t easy. But there’s increasing hope, dataviz professional Nicole Edmonds assures: “leadership is starting to understand the breadth, power, and importance of their data assets. I also think, with the advent of Data related leadership positions, things are changing.”
  • Engage your naysayers: As dataviz professional Bridget Cogley points out, “it helps you understand what resistances exist and why. If you can turn them or find a way to sate them, you have a much clearer path forward.”
Illustration by Natalia Kiseleva (eolay13)

2. Spark Emotion & Excitement (it’ll be awesome!)

Let’s be real: humans aren’t data-driven. We’re emotion-driven. Why fight it?

  • Appeal to emotion/vanity/inspiration: We say we want the facts, but sometimes we want the warm fuzzies. (And then maybe some facts.) Dataviz professional Jason Forrest stumbled onto this in his work at McKinsey, “I’ve gotten as much feedback from people based on my artier work as on my dashboards. What I’m finding is that they are beginning to play off of each other, and THAT is what begins to actually change some bad habits and helps people explore better ones.” We don’t have to throw rigor out the window. But, taking a cue from Jason, maybe the best way to get people excited about dataviz is to, well, get them excited. And the most effective way to build excitement is by making visualizations people can see themselves in. For example:
  1. Create maps highlighting people’s hometowns (“Here’s me in the data!“)
  2. Make a visual history of the org (“Here’s my contribution to the bigger story!“)
  3. Highlight stories of people that the data represent (“Here’s who I help!“)
  4. Use charts people know and love (“Here’s something I can understand and explain to others!“)
  • Start small & demo great data visualization process: Starting small can create big change. Brandon Hecker experienced this first hand: “Rolling out a simple design thinking process for analytics changed the entire company culture and mindset from a quantitative-focused, technical approach to an empathetic approach … I originally just started this on my own, and as soon as my company caught onto what I was doing and how successful it was, the company started coming together from all over…” You don’t need to restructure an org. People already want to be part of something awesome. If you can just show how dataviz can be awesome — even in small ways — you’ve made huge headway.

3. Make the ideas behind great dataviz culture shareable

We have to articulate a great dataviz culture before we can realize it.

As dataviz professional Elijah Meeks points out, we need white papers, posters, catchy slogans and other sharable material if we want people to receive and retain the tenets of great dataviz culture. Facebook had “Move fast and break stuff” — what about dataviz?

Pithy slogans are already floating around out there:

We’ll need the whole community to create more — and to share them. Here’s what emerged during this Data Visualization Society discussion:

  • What good is data-driven without good data-vision?
  • Data visualization isn’t a chart. It’s a process.

So, what can I do to harness this opportunity?

If you’re dataviz professional, your role in developing a dataviz culture is clear:

  1. Start small and personal. Start with what people know, start with one leader, or start with people’s emotions.
  2. Make dataviz a process, not a product. Put people’s experiences and needs at the heart of your work. Get constant feedback. (This isn’t new: it’s called human-centered design, and there’s lots of support for it.)

If you’re not a data viz professional, your role is just as important:

  1. Help allocate space for dataviz process. You already allot space for data science and do so precisely because you don’t yet know the data’s properties. In that spirit, give dataviz practitioners space to research, explore, and test, precisely because you don’t yet know what visual form will most powerfully unlock and communicate the data’s insight.
  2. Focus on questions, not chart requests. You’ve got nuanced insight into your own work and it’s key questions. By focusing on those expert questions and challenges with dataviz professionals (instead of leading with a chart request), dataviz tools will better reflect and support your expertise. It also helps you communicate your impact to others.

As data floods in, the orgs 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.

Data viz professionals are already leading the way. The question is: will your org do what it takes to harness this opportunity?

Thanks to the Data Visualization Society members who 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.

A special thanks to Eleanor Collier, Elijah Meeks, and Alyssa Bell who helped craft and revise this article. And hats off to dataviz-illustration wizard Natalia Kiseleva.


The Journal of the Data Visualization Society

Thanks to Tricia Aung and Alyssa Bell

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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