Bringing data to the masses requires no less. It mandates that companies bring on board an expert who can effectively translate left-brain data into right-brain art in a way that both sides fully understand.
To understand why, think about how statistics and visual art are fundamentally different. Statistical analysis and visual art have histories that go back for centuries, if not millennia. Each practice has had its pioneers: Bayes, Laplace, and Gauss on the statistical side, and Michelangelo, Rembrandt, and Picasso on the artistic side. But where do the two meet?
When it comes to finding geniuses who are truly good at understanding the beauty of mathematics and art in combination, only a few stand out. These include the Renaissance artists Dürer and Da Vinci, as well as the more modern M.C. Escher.
But not every mathematician has an eye for visual beauty. And not every artist is fluent in the language of statistics. To truly present numbers in a visualization that is effective and valuable, people must be able to translate sets and functions into figures that immediately present guidance and information to an appropriate audience.
Traditionally, this has meant creating a bar graph that simply shows a blocky and proportionate comparison of basic metrics. As these visualizations have evolved over time, they have typically become larger and more complex, as we have seen with social network analysis and word clouds. And when the audience for data only consisted of similar left-brained quantitative experts, this was sufficient.
But an additional level of nuance to data visualization has barely been tapped. We have thousands of years of experience in equating various colors with various levels of urgency or context, yet rarely take advantage of those cultural cues. Color theory shows how context, weather, and other mitigating circumstances should change color and shades. Architectural perspective can provide guidance on the shapes and structures that the eye finds most appealing.
But these concepts are rarely taken into account for data visualization. In short, the visual nuances of design, perspective, color, and other artistic components are still in their infancy when data visualization is concerned.
Thought leader Stephen McDaniel is no stranger to traditional analytics, having written SAS for Dummies and having served as an analytic consultant and instructor to many organizations. When I asked him about visualization, he replied, "Visual analytics is a legitimate field."
This view has been reflected in the business intelligence and analytics world, not just by visualization and data discovery specialists like QlikView and Tableau, but by pure-play platforms such as SAS and MicroStrategy, and even megavendors like IBM and SAP, which have poured their development dollars into better applications and visualizations.
Why are they all going in this direction? End users are demanding better and more creative visualizations than the standard charts and graphs that were used during the first 30+ years of relational databases and standard business intelligence. It only makes sense that as the artistic palette changes, the skills to wield that palette will change as well.
This is not to say that the traditional data scientist or data analyst doesn't have a place in the new world of visualization. On the contrary, companies need data-savvy individuals more than ever. But because of this transformation of data visualization capabilities, and the increased demand for data-driven decisions, companies need to consider a new specialist on their team: The data visualization expert, fluent in both quantifying and qualifying data to support business goals.
Do you agree or disagree? Read Jonathan Schwabish's Counterpoint and share your thoughts below.Related posts: