But that's not enough in some applications. Recently a large New York City bank told Evelson it needed someone with deeper skills to visually present a sophisticated and comprehensive portfolio analysis -- analyzing thousands of clients with various types of investments and risks. Although the bank had "all the right tools and technologists," he says, it was looking for someone with a specialized understanding of how the brain reacts to and digests visual information.
"It was not about the technology of data visualization, but the psychology of visual perception," he says. The bank wanted someone who would know which types of visualization techniques work best for different types of data, as well as the limitations of certain techniques. For example, "a significant proportion [ about 7%] of the population is colorblind," he notes. "So maybe they shouldn't exclusively rely on color."
Dataviz: A brief how-to
Know your data, know your audience, and determine the message you want to communicate.
Reduce the data to what's needed to communicate your message, remembering that, without context, numbers mean little.
Determine the best means of expression.
- Some quantitative messages are best communicated with words, some with tables of numbers, some with specific graphs -- bar, line, scatter plot, etc. -- and some with a combination.
- These principles aren't intuitive; they require training into how our eyes and brains process visual information. Consult a data visualization expert on this step (or train yourself or your staff).
Design the display to communicate simply, clearly and accurately.
- Don't include anything that isn't data unless it's needed to support the data.
- Avoid unnecessary color variation and visual effects, or even grid lines in a graph when they aren't needed.
- Make non-data elements only visible enough to do their job; they should never overshadow the information.
- Visually highlight information that's most important to the message.
Suggest actions in response to the data. Most quantitative messages aren't presented merely to inform but also to motivate a useful response.
Source: Stephen Few
The bank did end up bringing in a professional -- but as a part-time consultant rather than a full-time staffer, a trend that analysts say will likely be repeated in many companies, even as big data heats up. A third option is to outsource such data visualization projects to boutique consultancies.