Data analysis & data visualization best practices for Business Intelligence (P3)

Heard of the KISS principle? You probably have – Keep It Simple Stupid. For the most part, this concept can (and should) be applied to data visualization and Business Intelligence (BI).

As a general rule, data is most easily consumed, understood and acted upon when its graphical representation is in its most elementary form. Flashy visualizations may grab a report consumers’ attention, but often do so at the expense of the core message, trend or pattern that the imagery is intended to convey. Analytical reports are inherently detailed and complex. Data visualization, therefore, must aid people in interpreting, understanding and analyzing that information to support fundamental decision-making. Infographics of any type are made redundant if they distract or detract from the underlying data.

For more on the correct application of data visualizations, check out our formative blog posts Data analysis & data visualization best practices for Business Intelligence (P1), Data analysis & data visualization best practices for Business Intelligence (P2) and Business Intelligence: Intuitive vs cool data visualization and infographics.

Today’s data visualization tip: Interactivity / animations are good and bad

Moving components and animations within a graph, chart or map grasp the attention of users, but if too overt, can obscure other important information. So stick to the basics, and continually question their intended purpose, as a method for reassessing whether the desired outcome or affect is being achieved.

Beneficial interactivity

However, it is also true that certain interactivity can deconstruct complex data sets, enhance information absorption, connect quantitative with qualitative data and real-world events. Features with these constructive capabilities include:

• Rollovers
• Pop-ups
• Zoom
• Drill down or through
• Filters

Beneficial animation: Demonstrating change over time

Full-blown animations are rarely necessary in the context of BI. Demonstrating change over time is the exception that proves this particular rule.

Traditionally, line graphs have been used to demonstrate change over time. A line graph is perfectly adequate when solely visualizing time-based data such as number of sales per month. However, a line graph is inadequate for measuring change over time AND comparing the relationship between two or more values.

Data visualization guru, Stephen Few, explains the usefulness of animation (moving objects within a chart) in his 2007 paper Data Visualization Past, Present, and Future:

“Consider the correlation between marketing expenses and resulting sales. The best way to examine this correlation at a particular point in time is by using a scatterplot, with marketing expenses measured along the X-axis (the horizontal axis), sales revenues along the Y-axis (the vertical axis), and a separate data point for individual items, such as one for each state, totaling fifty data points in all. If we want to see if the nature of this correlation has changed over the course of time, however, this correlation cannot be represented as effectively using a line graph, so what can we do to display it? The answer is that we can animate the scatterplot, allowing the data points representing marketing expenses and sales revenues for each state to move inside the scatterplot to show how these values have changed through time.”

Measuring change over time: Four types

Graphical animations can be used to measure four distinct characteristics of change over time, including:

• Trend: The direction of the movement of a particular value over time (eg: trending up or down)
• Pattern of change: The movement of a particular value over time (left, right, up or down)
• Speed of change: The rate of change of a particular value over a specified period of time
• Scale of change: The difference between a particular value at one specific point in time compared to another