There are prominent theorists and practitioners in data visualization that simply do not believe there is such a thing as a dedicated data visualization role in industry. For those critics there is no profession, only a skill used near the end of a long process performed by scientists, analysts and engineers.
In contrast, there’s a celebratory data visualization community that gathers for the Information is Beautiful Awards and looks to people like David McCandless as a thought leader. The more serious are in or allied with journalism, the more exotic might call themselves artists, and the freelancers and consulting firms that dominate this area might see themselves as a bit of both. In their case, catching an audience in an attention economy is a prominent requirement of their data visualization work.
Somewhere in between these two sides is a growing professional space referred to sometimes as “data visualization product”. It’s occupied by roles with different titles — I, for instance, am a Senior Data Visualization Engineer — who create custom data visualization applications that are more than just the product of industry tools but not quite as hand-crafted as data visualization in journalism or for public communication pieces. If we were to plot these various roles on a spectrum, it may look like this:
As I laid it out on this spectrum, today’s professional data visualization engineer uses theory and practice more closely resembling the conservative approach to data visualization. But that has more to do with historical priorities than it does practical concerns.
Data visualization used to be expensive, used to be difficult, used to be static, used to be supplementary. Many of the most prominent manuals and maxims of data visualization were either created when it was only static figures accompanying text or assume it still is. But for companies to succeed with data visualization they are going to need to break out of that archaic view of data visualization and push for the adoption and spread of principles associated with art, design and journalism.
While anyone who’s made a pie chart in Excel could reasonably be said to “do data visualization” that formulation ignores the emergence of the dedicated data visualization role. A recent survey of nearly 1000 data visualization professionals shows roughly a quarter identify their roles as primarily focused on data visualization. The emergence of that dedicated role is exciting but it raises three main concerns:
- Data visualization products are extremely conservative
- Data visualization as a professional focus currently lacks clear avenues for advancement, so bright people with ambition feel forced to transition into other science or engineering roles in order to advance
- Data visualization is underrepresented in leadership positions
We lack clear success stories for using complex data visualization in an industry setting. Sure, there are superficial lists of cool projects but these have no effort spent on identifying structurally what works and what doesn’t and why. Differences in these lists are attributed to taste. This leaves nothing concrete to refer to for creators or stakeholders to base their decisions on how to approach data visualization, which one colleague described as “the perfect blend of subjective opinion clothed in pedantic rules of human perception.” The only leadership comes from a conservative group of experts who release new books every year repeating themselves about how the only valuable charts are spartan bar and line charts with subdued colors. And when your job is on the line, you’ll pick those books over the coffee table books showing beautiful data visualization projects from around the world and throughout time that noticeably lack a section describing how those charts had any measurable success.
And because you’re hired to do data visualization but only asked to do the most simple version of it, jobs advertised as being about data visualization leave little room for technical innovation. The putative data visualization engineer need only spend a couple hours a week making bar and line charts, and therefore has more than enough time to work on the important parts of an application: the API, the build process, the data stores, and everything else but the actual communication of data using graphics. That’s a story I keep hearing here in Silicon Valley and a story that ends with the teller either asking me how to fix it or telling me how she’s transitioned out of data visualization into a different position.
I’m not saying we should only make bespoke data visualizations that are heavy on aesthetic quality and light on impact. I’m suspicious of those, too. But the conservative stance is not the only path to impact. Sure, there are times when the simple answer is right, when busy people need to make decisions immediately and need simple, clear charts. But that limited definition of impact ignores real situations in industry where busy people struggle with complex questions and need to be supported by complex datasets that need equally complex charts. Likewise, those same busy people and their complex solutions need to be accurately communicated to their busy team in a way that doesn’t unnecessarily reduce the complexity of the problem. It is not a coincidence that this echoes the recent call of Giorgia Lupi for a move toward “digital humanism”.
The development of methods and products to do that exploration and explanation in industry is very different than the core data visualization skills and literacies that need to be taught across an organization. Every company needs to make sure their charts are clear and effective, and many of those charts are going to be line charts and bar charts. But if you only think of data visualization as a skill to supplement analysis and research, then you won’t be able to think of innovative ways to adapt novel data visualization techniques into your business. In the same way that we’ve all come to agree that machine learning and AI need to be a part of every business plan, we need to also accept that complex data visualization does too. These new data visualization roles need to move along the spectrum, toward Giorgia and her tribe. Maybe not so far that our dashboards are winning IIB awards, but closer to the principles of engagement, storytelling and complexity.
Data visualization in industry needs to shake off its emphasis on basic skills learning and integrate complex and challenging new techniques: that means fancy chart types that may at first look like art, annotations like you might see in news articles, and scrollytelling like that found in so many landmark data visualization projects. The people who are going to do that are data visualization professionals.
For that to happen, we need to give the people doing data visualization in industry the tools they need to succeed. That means giving them ways to measure impact that apply to complex data visualization. It means embracing expository data visualization in professional practice by recognizing that dashboards and bare charts are not always the solution. Finally, it means giving professional data visualization practitioners a path to advancement. Maybe that’s just hearing success stories. Maybe it’s Chief Data Visualization Officers in the same vein as Chief AI Officers. The organizations that can do that are going to attract the best data visualization talent, who are passionate about communicating the patterns in data. More importantly, the organizations that can do that are going to keep and enhance that talent to produce ever more compelling views into the data that determines their success or failure.