By Stephen Few
As with many fields that experience rapid growth, the meaning and practice of data visualization have become muddled. Everyone has their own idea of its purpose and how it should be done. For me, data visualization has remained fairly clear and consistent in meaning and purpose. Here’s a simple definition:
Data visualization is a collection of methods that use visual representations to explore, make sense of, and communicate quantitative data.
You might bristle at the fact that this definition narrows the scope of data visualization to quantitative data. It is certainly true that non-quantitative data may be visualized, but charts, diagrams, and illustrations of this type are not typically categorized as data visualizations. For example, neither a flow chart, nor an organization chart, nor an ER (entity relationship) diagram qualifies as a data visualization unless it includes quantitative information.
The immediate purpose of data visualization is to improve understanding. When data visualization is done in ways that do not improve understanding, it is done poorly. The ultimate purpose of data visualization, beyond understanding, is to enable better decisions and actions.
Understanding the meaning and purpose of data visualization isn’t difficult, but doing the work well requires skill, augmented by good technologies. Data visualization is primarily enabled by skills—the human part of the equation—and these skills are augmented by technologies. The human component is primary, but sadly it receives much less attention than the technological component. For this reason data visualization is usually done poorly. The path to effective data visualization begins with the development of relevant skills through learning and a great deal of practice. Tools are used during this process; they do not drive it.
Data visualization technologies only work when they are designed by people who understand how humans interact with data to make sense of it. This requires an understanding of human perception and cognition. It also requires an understanding of what we humans need from data. Interacting with data is not useful unless it leads to an understanding of things that matter. Few data visualization technology vendors have provided tools that work effectively because their knowledge of the domain is superficial and often erroneous. You can only design good data visualization tools if you’ve engaged in the practice of data visualization yourself at an expert level. Poor tools exist, in part, because vendors care primarily about sales, and most consumers of data visualization products lack the skills that are needed to differentiate useful from useless tools, so they clamor for silly, dysfunctional features. Vendors justify the development of dumb tools by arguing that it is their job to give consumers what they want. I understand their responsibility differently. As parents, we don’t give our children what they want when it conflicts with what they need. Vendors should be good providers.
Data visualization can contribute a great deal to the world, but only if it is done well. We’ll get there eventually. We’ll get there faster if we have a clear understanding of what data visualization is and what it’s for.