Engineers like numbers. Engineers like problems that can be solved with numerical analysis. Engineers like when others agree that their numbers are correct. However, all too often Engineers fail to clearly communicate their ideas, analysis, and solutions in a manner that quickly informs, educates, and persuades their audiences. I would know; I am an Engineer.
Presenters commonly overlook good information design in their presentations. Instead, they focus on providing the maximum amount of information and data in a manner that allows the audience to fully appreciate not only the solution but also the process of the analysis. In their attempt to wow the audience with slides dominated by tables, charts, graphs, best-fit lines, major and minor grid lines and the like, they instead produce confusion and lack of interest. I will be the first to admit that I am guilty of such techniques.
In Edward Tufte’s work on information design, “Visual Display of Quantitative Information” – yes it is indeed as interesting as it sounds – Tufte discusses Data-Ink and Graphical Redesign. In order to achieve maximum impact, Tufte outlines five principles for data graphics that can lead to significant improvements in graphical design: 1) Above all else show the data, 2) Maximize the data-ink ratio, 3) Erase non-data-ink, 4) Erase redundant data-ink, and 5) Revise and edit. To help clarify, Tufte describes data-ink as “the share of the ink on a graphic that presents the data-information”; it is “the non-erasable core of the graphic.” The key and the challenge of this topic is finding simplicity.
Tufte provides a great example of how to erase redundant data-ink within reason. Consider a simple bar chart with a single bar that is shaded and displays the value of the data point at the top of the bar. The height or value of the bar chart in this simple example is identified in six separate ways. Five of those ways can be considered redundant and removed, and the important data will still be present. The six ways include, 1) the height of the left vertical of the bar chart, 2) the height of the right vertical of the bar chart, 3) the height of the shaded region of the bar chart, 4) the vertical position of the horizontal top of the bar chart, 5) the vertical position of the value on top of the bar chart, and 6) the numeric value itself. Removing redundant information creates clearer presentation and more effective communication of a presenter’s ideas.
For most, this is likely not the most exciting of topics. However, for someone who works heavily in numerical analysis and who must convey outcomes to audiences of varying backgrounds, these suggestions on good information design are priceless. Does anyone else struggle in the area of good information design? Have you ever been complimented on your information design? Any other suggestions of how someone can improve their ability to display quantitative information?
Tufte, Edward R. “Data-Ink and Graphical Redesign.” The Visual Display of Quantitative Information. Second ed. Cheshire, CT: Graphics LLC, 2006. N. pag. Print.
Andrew, I consistently run into this issue with presenting data. I have very detailed raw data that I churn into detailed forecasting reports that are tools that I use, however are too in the detail for upper management to follow. I have to constantly parse data into the simplest form then highlight what is meaningful to get my point across.
What I have found helpful is looking at the reporting that I have created and thinking to myself if I were my manager would I be able to tell directionally, “is this good or bad?”, “are we trending up? or down?”, “which data points are the drivers?”, “what are the outliers?”. If I can answer the above questions with my report, which takes some editing or Edward Tufte’s 5th: principle of Revise and Edit; then I feel the report is ready to be viewed and used for a tool.
Andrew
Although I rarely am required to officially present the data that I am working with, it is imperative that I am able to convey the results of my work. To that end, I struggle with the best methods for communicating the 10,000 view of the results. I, like you, rely heavily on data and find that the best way for me to interpret this data is to have an opportunity to see all of the information, oftentimes redundantly. However, I am realizing that there are likely few people who are empowered by having access to all of the data in a visual form. Interestingly, I had not considered the impact of the redundancy of the types of approaches I have used to present the information I am working with, so I need to focus of a lean method of information delivery. Thank you for the post.