What do they really want to know? – Graphical presentations

Over the years, a great deal of work has been carried out to determine the kinds of graphical presentation that make data most easily understood and here are some practical examples:

Numerical proportions

This shows three ways of displaying the fact that certain workloads are the largest contributors to the total loading on a system. The table of numbers, while true and accurate, is difficult to assimilate quickly. The horizontal bar chart shows the relative magnitudes at a glance, but does not convey the additional information that the elements add up to a particular total. The pie chart shows the relative magnitudes and also conveys the information that the elements account for "everything".

Areas and scaling

Some variables, for example different categories of CPU utilisation, can logically be summed to present a total value. If plotting two (or more) such variables over time, it is good practice to stack the individual values so that this total value is clearly displayed. In the example shown, the measured values are hourly aggregations of what is in fact a continuous variable, namely the CPU utilisation over time.

The fact that the variable is continuous is most clearly brought out by displaying the results as an area graph rather than as stacked bars. Use stacked bars when the values are snapshots made at specific times, for example the number of users logged on at particular times of the day.

If possible, show the results against a fixed vertical scale, rather than accepting whatever automatic default your graphics package determines for you.

There are two reasons for this:

• The viewer can see at a glance how much scope there is for a potential increase in the value of whatever is being presented.

• If the graph is going to be updated by the use of Automatic Reporting technology, fixed scaling contributes to consistency between one version of a report and the next. This makes it much easier to compare different versions of the same report that have been produced at different times.

Magnitude or Variability?

You may be using the same set of data to emphasise two (or more) different attributes of the measurement in question.

For example, you might want to display a graph of CPU utilisation for at least two different reasons:

•         To show how large (or small) the utilisation is on average
•         To show how the utilisation varies over time.

A good rule of thumb is:

•           To emphasise magnitude, use an area chart
•           To emphasise variability, use a line chart.

On Monday I’ll be taking a look at automatic trending.

Rich Fronheiser
Chief Marketing Officer