In our inaugural OpenBI Forum column of April 27, 2006, we discussed the utility of the common and unsexy dot plot for business intelligence (BI). After first acknowledging the seminal contributions of Tufte and Cleveland - let the data tell the visual story - we settled on juxtaposition, scale, performance, size, grouping and overlay as critical considerations for the construction of effective dot plots. We then illustrated the concepts with real-world examples. This column expands on that thinking using a similar format to discuss dimensional graphics - visuals that examine relationships between variables x and y conditioned on the values of another attribute (or set of attributes) z. The z attributes are in fact the dimensions or panels by which we wish to view the relationship between x and y. Used judiciously, we feel that dimension graphics can be an invaluable adjunct for performance management, providing a foundation for the evaluation of company performance.

The cornerstone of our dimensional thinking is the work on Trellis graphics by William Cleveland, a statistician with Bell Labs in the 80s and 90s (see http://stat.bell-labs.com/wsc/). Subsequent interest has spawned other names for the Trellis approach, including panel, lattice and small multiples. Trellis graph patterns, reminiscent of a garden trellis, depict relationships between two variables (x and y) conditioned on one or more dimensional attributes, and derived from the need to visually investigate complex multivariate relationships with a response variable. Each panel of the trellis represents the graph of a relationship between x and y (the response variable) for a single combination of the conditioning variables. An example of reasonable trellis candidate is the relationship between cholesterol level (x) and the odds of cardiovascular disease (y), conditioned on age category, sex and race. The trellis in this case would depict a separate panel graph for each combination of age category, sex and race that occurred in the data. The individual plots can be many types, including scatter, xy plot, curve plot, quantile, strip plot, and dot plot. A defining characteristic of the Trellis approach is that of common scales for each panel in the overall graph, thus allowing consistent visual comparison between panels. Along with the commonality in panel scales is the flexibility to control the layout of panels to rows, columns and pages, promoting clear graphical comparisons.

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