With the advanced offerings of many BI solutions nowadays, you are often met with more types of chart than you know what to do with. But, remember, they are there for a reason : each one is useful in its own way. The trick is just knowing which one to use, and when. Hopefully this article will give you a better understanding of some of the more common charts you might be using. To the casual user it may appear that different charts can simply be used interchangeably, and this is often the case, but not always the best solution. Experts have come up with guiding principles that can be applied to achieve maximum data visualization effectiveness. This also comes at a time when viewing data on mobile devices is becoming more and more popular, and this should be taken into account too.
Basic charts consist of:
- Bar charts (and column charts)
- Line charts
- Point/Bubble charts
- Pie charts
Why are they considered basic? Because in their most basic form they measure one element against one or more quantitative metrics associated with that element.
Take the bar chart. This should be used to display one element (product), against one quantitative metric (profit) or several quantitative metrics (profit and number of items sold). Because bar charts can become difficult to read if you add too many metrics, they work better if you use efficiently the space you have available.
Time or date dimensions are likely to be better displayed using either a line or a point/bubble chart. Just as with bar charts, multiple quantitative metrics can be displayed in a single chart, assuming that the chart does not get too crowded. Adding a mean statistic line onto a line chart can also be easier to read than for example adding one to a bar chart, and can show how far a measure has deviated from the average. However it is important to remember that these types of statistics should only be added in cases where they contribute positively to the understanding of the data, and only where they are justified - there is not much point adding a trend line to 3 data points or less because it implies a trend that probably does not exist!
The point/bubble chart is in it's simplest form basically a line chart but without the lines. It plots each data point as the line chart does and just does not connect them with a line. They are useful for displaying a single quantitative metric, but can be confusing if trying to display multiple metrics, unless the software you are using is designed particularly well. Ways of doing this could be encoding by color, or size for example. Joining the points on a point chart using a curved line can be visually appealing but also misleading in terms of accurate data analysis. This is because a curved line suggests that a polynomial relationship exists - which may or may not be true. Equally turning the lines of a line graph into curves can have the same effect. Avoid it if it changes the meaning of your data!
Last of the basic charts is the pie chart. Pie charts are ideal for showing data that has a number of components and only one quantitative metric. They require that the sum of the component values add up to 100% and that there are no negative components. For data points that are very small, grouping them into a category together ("other") is a good idea, to avoid crowding and confusion. Visualization gurus suggest that the maximum number of data points to display on a pie chart is between 7 and 10, although physically you could display any number of segments.
There are other types of chart which are perhaps less basic, but equally as common in dashboard apps.
- Bullet charts
These can all be used to measure actual vs anticipated performance, current vs previous performance, and deviation from expected trends or benchmarks. They are also useful for giving a top-level overview for those that don't have the time or desire to examine data in great detail.
Sparklines are designed to show overall trends, as well as highlight the most recent data point in the context of the history of the data. Sparklines should be used to provide immediate and unambiguous trend comparisons among related data entities.
The bullet chart is common for displaying a quantitative value and a horizontal scale. This version of a bullet chart adds additional context by displaying a threshold via the black vertical line, and also encodes a qualitative measure using color. When should you use this type of chart? Comparing related metrics with a bullet chart can give you a quick snapshot of your data, so when you are looking for an instant comparison this is a good choice.
The guage uses a needle to indicate the current value of a metric. As with bullet graphs, guages make visual comparisons quick and easy. Although often visually appealing, these visualizations do not tend to use dashboard space effectively, so try to use them when space permits, and not at the expense of, say, bullet charts.
A problem you may come across in your dashboard once you have chosen all your charts carefully is that of data update. When you update data, this could skew your results considerably, and render some of your visualizations less effective. Unfortunately there is not much that dashboard designers can do about this, it is just a matter of you as the creator to test the extremes of your data set, if possible, and choose your visualizations with the results in mind.
Of course Bime incorporates all the types of chart I have discussed above, plus more. So next time you use Bime to analyze your data, I hope you are able to make the best visualization choices and use Bime to its full potential!
Don' forget to check out Which Chart Should I Use and When? A Guide to Dashboard Charts Pt 2