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.

Have you heard? Data visualization has gone mainstream.

As with other areas of practice that have entered general public awareness (think data science and machine learning), there is a ballooning of interest and expectations. Not all of these expectations match reality.

Some may see data viz as a mysterious field best left to specialists or to big companies with deep pockets. Other times, data viz can be misunderstood as frills to the more “serious” task of data analysis.

In a Data Visualization Society (DVS) discussion, we busted some common myths that drive data viz people crazy — here’s our take on possible remedies.

Myth #1: Data viz is all about making things pretty.

It is true that data viz can result in beautiful, elaborate diagrams. Some even use the display of data patterns as a tapestry for art. However, there is a whole spectrum of data viz applications; only some are concerned with beauty as the primary focus. Sometimes, beauty in the data viz design serves as an instrument to draw the users’ attention to key features.

Data viz is an interdisciplinary practice, drawing from fields such as graphic design, statistics, human-computer interaction, and data management. Technical steps like acquiring, parsing, and refining the data are all part of the process. As one DVS member shared, there have been countless times where he and his team caught data flaws in the midst of the data visualization process and saved the company from making huge mistakes.

Let’s break down some of the steps in creating a data viz. In the business setting, data viz practitioners have to first consider the target audience, the problem at hand, and corporate goals. To do that, they often spend time investigating to get at the heart of end-users’ true goals (e.g. is it merely to indicate the drop in sales or to highlight why there was a drop?) Then, there is figuring out how to answer the business questions with data, considering what to do with missing or unclear data, and doing checks to ensure that the right information is conveyed with the data viz.

By the time the data viz is produced, most of the cognitive heavy-lifting has already been taken care of. Unfortunately, there is the tendency to misinterpret results that are easy (or easier) to understand as being easy to produce. This brings us to the next myth.

Myth #2: Good data viz is easy-peasy to achieve.

Thanks to the rise of tools like Tableau and Datawrapper, as well as programming packages like ggplot2 or seaborn, data can be visualized in a few lines of code or even just a number of clicks. In this sense, the last mile of the data viz process can be easy and simple.

But the fact is, a number of calibrations go into play before reaching that stage, and the whole process is often iterative. The most visible part of creating data viz is often the most overstated part. Scott Berinato, senior editor of the Harvard Business Review, sums it up:

Good design isn’t just choosing colors and fonts or coming up with an aesthetic for charts. That’s styling — part of design, but by no means the most important part.

Data viz practitioners also have to ensure that they don’t unintentionally obscure, distort, or misrepresent with their visual models. These steps may be hidden from the user perspective but are just as crucial.

In a retrospective showcase, the Economist demonstrated how choosing the wrong visualization method can create a misleading chart on post-Brexit referendum attitudes. By connecting values from individual polls to show an overall trend, the original chart gave a false impression that respondents had a rather erratic view.

Like many other dataviz practitioners,Keisha Carr,is aggrieved when people think all she does is tinker with aesthetics. In actuality, she researches books and articles, analyses the data, and expends much energy considering and eliminating possible design options before arriving at the final data viz product. Most people don’t see the discarded drafts.

The challenge is compounded when we shift from developing one-off data viz pieces to a reproducible system.

Myth #3: Data viz is the end goal.

With the stunning innovations in data viz, we can end up paying excessive attention to the look and feel of data viz products without considering a broader strategy. This sometimes happens when dashboards become the corporate “flavor” of the month. A client or project lead may be compelled to recreate an engaging data viz product seen elsewhere — even when it may be at odds with the project needs or data features.

Credit: Tom Fishburne, the Marketoonist

But we are missing the point when we obsess about the visualized output. Data viz is about using data to inform decisions and initiate the next steps. Consider dataviz as a bridging tool: Whether you’re visualizing qualitative information or you’re plotting quantitative information, you’re working towards an outcome. It could be to inspire change. For instance, in Singapore, a city-state known for nudging its residents towards behavioral change, dataviz is used to encourage more prudent energy and water usage. Utilities bills have been redesigned to include consumption bar charts showing how your previous month’s usage compare to neighbors’ and the national average.

Extract of a sample utility bill in Singapore

Another outcome of dataviz could be visual discovery. Say, the management team wants to have a better idea about why public interest in your company is suddenly surging — you don’t quite know what you’re looking for. You may want to mine and visualize your data to see what trends and anomalies emerge. Dataviz practitioners Joshua Smith and Amanda Makulecfind that the more successful designs, particularly for exploratory data viz, are those that focus on the end-users and how they will use the viz to achieve their objectives.

Myth #4: Adopting data viz requires great infrastructure investment and expertise

Similarly, it’s easy to think that data viz is less than accessible, especially given the widespread growth of interactive data tools and advanced visualizations. A related version of this myth includes thinking of data viz as the result of elaborate and expensive processes.

However, this needs not be the norm. Ultimately, data viz is about contextualizing data for people’s consumption. Anyone who is interested in communicating data effectively, from those working in small grassroots organizations to schoolchildren, can benefit from data viz practices.

Even the most basic examples can be elevated with the right working process and understanding of dataviz methodology and concepts. Learning to remove visual details that distract instead of inform, or how to use color cues for emphasizing trends, even in something as familiar as a table of figures, allows anyone to craft a more compelling message. Creating a well-designed data viz also doesn’t require fancy software — it can even be done on the back of a napkin, as shown in the example below.

One of the winning entries in the “napkin” category of the 2019 World Data Visualization Prize, a hand-drawn chart showing the correlations between different development indices.

Below are some recommended resources from DVS members on getting involved in data viz

For a gentle introduction:

For a more in-depth study of the craft:

How can you help stop the spread of myths?

There are some steps you can take to help dispel the confusion, ordered from least to heaviest involvement.

1. Share your examples

If mentioning “data viz” returns you vague nods or blank looks, it always helps to have something concrete to point to.

DVS member Nik Eveleightries to come up with fun examples that are relatable to those around him. He built a dashboard for his local running club so they could check out their race history. With his kids, he collected data on a packet of sweets to run a mini project.

There is also the option of letting the results speak for themselves. Dataviz practitioner Wendy Smallfinds she hardly uses the term “data viz” but applies it regularly to solve business problems.

2. Speak their language

It helps to have a point of reference that people are more familiar with. Data journalist Duncan Geeretells people he “makes charts, but better”, while Data cartographer Philippe Rivièreshows people beautiful maps and jokes he “makes those, but worse.”

Other dataviz practitioners like Evelyn Münster and Natalia Kiseleva explain their data viz work by starting with examples from their audiences’ world like Google Analytics or supervisory control and data acquisition (SCADA).

Dataviz professional Andy Krackov describes his work in data viz terms of what people care about: its impact in helping organizations communicate their data findings.

3. Create a shared experience

Dataviz practitioners like Charles Saulnier and Keisha Carr suggest iterating through designs with stakeholders. One way to guide the conversation is to highlight the pros and cons of each data viz option.

Freelance designer Ben Oldenburg finds it useful to build relationships first, in order to boost people’s buy-in for finding a better way forward with data viz. For Tableau consultant Bridget Cogley, a lot of time is spent listening to clients to build trust before attempting to convince them that there’s more to it than just “charts on a page”.

And sometimes, the best way for someone to truly understand something is by trying it out. In Dataviz professional Victor Pascual’s experience, rolling out a basic course where clients get hands-on with data viz helped them better appreciate the work required to develop a meaningful visual narrative.

Towards a new era

We develop specialized skills when practicing data viz, and these are of value. As the data viz community expands, it is important that we stamp out misconceptions that stand in the way of the field’s maturation and professionalization. If the last decade was about data visualization going mainstream, may this new decade be about its impact.

Thanks to the Data Visualization Society members for contributing to the discussion, including: Amanda Makulec, Alexwein, Andy Krackov, Annette Greiner, Ben Oldenburg, Bridget Cogley, Charles Saulnier, Chris Lysy, Christoph Nieberding, Davestark77, Duncan Geere, Evelyn Münster, Hannah, Irene de la Torre, Jack Merlin Bruce, Jane Zhang, Jeff Harrison, Joseph (Geolic), Josh, Keisha, Min Hwangbo, Natalia Kiseleva, Nicole Edmonds, Nik Eveleigh, Pei Ying Loh, Philippe Rivière, Polinas86, Rasagy, Sarah Eisele-Dyrli, Victor Pascual, Wendy Small, Yuna Celina

Alexandra is an analyst-designer who lives in Singapore. She creates stories and comics with data to help clarify the complex.