The 7 Biggest Issues Data Visualization Faces Today
By: Alli Torban
Tags: Design, Data, Topicsindv, Data Science, Data Visualization

The 7 Biggest Issues Data Visualization Faces Today

According to the members of the Data Visualization Society…

On a dedicated channel, #dvs-topics-in-data-viz, in the Data Visualization Society Slack, our members discuss questions and issues pertinent to the field of data visualization. Discussion topics rotate every two weeks, and while subjects vary, each one challenges our members to think deeply and holistically about questions that affect the field of data visualization. At the end of each discussion, the moderator recaps some of the insights and observations in a post on Nightingale. You can find all of the other discussions here.

As a data visualization practitioner, it’s easy to feel isolated. We’re mostly swimming alone in our organizations or as freelancers. But in February 2019, we boarded our ship: the Data Visualization Society. Now that we’re together, what do we do? The founders have a vision to steer us toward a unified community that lifts up its members through collaboration and shared resources.

So, every week on the member Slack, we’re asking a different question that challenges members to think about data visualization issues deeply and holistically.

But what are those issues? For our first week, we asked our 3,000+ members:

“What do you think the most important issue in data visualization is?”

The answers and open discussion ranged from light-hearted to deeply concerning. They mostly landed in three main groups:

issues around data,

visualization in practice, and

and the general profession.

We’ve gathered these topics into three main themes so that the conversation doesn’t just benefit our membership, but can also be useful to the broader data visualization community. When I first started, it was difficult to know what a typical workflow looked like to create a visualization. Having access to a centralized community would have been helpful! :) I’m glad we’re assessing these issues now so we can be more supportive to those entering and actively practicing in the field.

Here are the biggest issues that we currently face, according to our members.

Data

Data visualization is often framed as a solution to the data-access problem, and most professionals who have a job title that includes “data visualization” spend an inordinate amount of their time not visualizing that data but cleaning and processing it. So it’s no wonder many of the concerns expressed by our members focused on data. Importantly, though, the concern wasn’t about processing data. It was how organizations deal with data, how to teach about data and how to deal with bias in data.

1. Organizations

Why do they spend so many resources on data collection without a plan?

Why do they spend so much time competing to collect the most data?

Why do they trust the raw data over the visualization?

How do we create and integrate more data translators with domain knowledge into organizational teams?

As member SanPaw put it, “I see many dazzling visualizations but very few provide insights or lead to meaningful insights. Having someone on the team with domain knowledge and understanding the business issues helps a lot.”

2. Teaching

Why aren’t teachers taught how to use data visualization techniques?

How do we prioritize teaching kids how to read a data visualization that isn’t a map?

Iris Morgenstern is working on bringing viz to teachers: “I am teaching future teachers how to use data visualization for their professional life. When they start they have no idea what DataViz has to do with anything. But in the end, many of them are delighted to see how much more they get out of their research projects.”

3. Bias

How do we raise awareness around bias in data collection methods?

How do we get more people on board with visualizing uncertainty, beyond the academic space?

How can we be more responsible in how we speak for the data?

Mike Cisneros says, “It is too easy and common that we wittingly or unwittingly relinquish responsibility for the quality of our work, be it in terms of how reliable the data is, how clearly it is presented, whether there’s a bias in the presentation reflecting anything other than the truth of the data, whether there are unsustainable claims being made, and so on.”

In Practice

The second category of concern was centered on practice. The Data Visualization Society is a professional society, so it only makes sense that we’d be particularly concerned about our practice. By this, our members typically referred to the techniques and tools that often define their roles. This includes dealing with how aesthetics and science overlap in the visual display of information, as well as how data visualization cannot be evaluated objectively with the kinds of performance tests in place in other technical fields, its value and impact are tied to its reception by its audience. On the tool side, there’s general anxiety about the sheer number of tools and how those tools enable users to make questionable data visualization decisions.

4. Technique

How do we balance beauty and understanding?

How do we explain to clients that “it depends” (meaning that the right data visualization or right technique is context-dependent)?

How do we create standards for accessibility?

How do we prioritize designing with our audience in mind?

One member describes a common scenario: “It feels like [my coworkers] want some hard rules on what and what not to do whereas I’m more inclined to say, for the most part, ‘It depends on the data/scenario!’”

5. Tools

How do we help people create good charts when modern software makes it so easy to make charts quickly?

How can we help those in the field not feel so overwhelmed with the number of tools to learn?

Bill Seliger said, “I think the underlying problem is that the democratization of data and easy learning curve on dataviz tools has far outpaced the education effort on how to present data.”

The Profession

Finally, there was a distinct focus on the profession as a whole. To outsiders, this might seem the least interesting aspect of data visualization. But if you scratch the surface, you’ll find that there’s little shared definition of roles and responsibilities, best practices and resources. That internal chaos is reflected externally in how we approach stakeholders to explain how we make data visualization, how we measure its impact, and how we justify further investment by our organizations’ leaders in the roles and resources necessary to perform effective data visualization.

6. Internal

Where can we come together as a central community?

How do we create centralized resources and best practices?

Where can we publish articles in a central place?

How do we learn from related disciplines (design, UX, etc.)?

How do we come to a common understanding of what data visualization is for?

How do we organize into sub-disciplines within data visualization?

Casey Haber added, “As a community, I think we need to study and integrate from related disciplines like design that have a long history with this problem.”

7. External

How do I effectively explain to other people what I do?

How do I make the case that data visualization is necessary?

Francis Gagnon says, “I usually say that information design, including data visualization, has a huge market and little demand. People don’t realize that theirs sucks and that they need professional help.”

Having a community and central forum for questions, discussion and self-reflection is an essential need for newbies and veterans alike. As we move forward, we hope to use these concerns as a guide to build a community that cultivates data visualization into a robust field. We look forward to your comments on any of the points above. If you have additional ideas, please join the Data Visualization Society and add your voice to the conversation.

There’s room for everyone!

To sign up please register at datavisualizationsociety.com/join

Elijah Meeks and Jason Forrest contributed to this piece. Thanks to Mara Averick for your keen editing.