How to Choose the Right Business Chart - A 3-step Tutorial
In this article, we will explain how to choose the right chart for your data. Or more precisely, for your message.
There are 3 fundamental rules that you should always follow when deciding which chart to use. If you learn these 3 rules, there is a good chance that you will get it right no matter what the context of your data is.
Will I display time-related data
What message do I want to deliver?
What type of data am I trying to visualize?
So, without further ado, let us start with rule no. 1.
#1 Display time horizontally, category items vertically
Let us focus on the most frequent business charts, such as column and bar charts, line and area charts, dot plots or pin charts, plus-minus (variance) charts, etc. Your first decision should always be whether to turn them horizontally or vertically.
Here's how you should make your decision:
Use horizontal charts for time series
Of course, you can visualize other types of data categories with horizontal charts. But you will probably run into problems with labelling, category spacing, etc. If you really want to make an exception to this rule, do it only when the number of data categories is very small and their labels (names) are very short.
Use vertical charts in all other cases
1. It is easier to display category labels
2. You can insert them into tables thus creating combined table-chart reports
Charts can also have circular (polar) orientation, such as pie charts or "spider" (radar) charts. Additionally, we also have 2- or even 3-dimensional charts orthogonally oriented in two-dimensional space, for example, scatter plots (XY charts) or bubble charts.
[Tweet "Use horizontal charts for time series only. In all other cases use vertical charts."]
Here is a summary of all possible chart orientations and when to use them:
|Orientation||When do I use it?|
|1.||Horizontal||Use only when your data represents time series (years, quarters, months, weeks, days or date/time values)|
|2.||Vertical||Use if you have discrete items (categories or structure data, such as products, customers, accounts, etc.)|
|3.||Circular||Avoid pie charts whenever possible. Use only when the number of categories is very small (2 or 3 categories)|
|4.||2D||Use when you have 2 or 3 variables in your data, such as in bubble charts or scatter plots|
#2 Choose the right chart for your message
BI/reporting/visualization tools usually offer a large set of chart types to choose from. So it is of utmost importance that you understand and predict the nature of the analytical task that your readers will perform when observing your charts. You should always ask yourself: What message am I trying to convey?
For example, you might simply want to show an interesting trend over time. Or enable your readers to compare sales values of different products. Yet another time your readers will need to understand the variance to budget, growth from previous year values, the contribution of specific products' variance to the total variance, etc.
Here is our cheatsheet that will help you select the right chart for the most common tasks that business users typically perform:
[Tweet "This cheat sheet will help you select the right chart for most analytical tasks."]
Structure analysis (category comparison)
Structure analysis is the most basic analytic task where the reader observes the displayed values and visually compares data categories to each other. These comparisons are usually of the following nature:
- Looking up a specific value (e.g. finding the sales value of a specific product)
- Comparing two values between each other
- Ranking (trying to establish the sequential order of one or more data categories)
- Recognizing patterns in the data (outliers or subsets of values with a specific pattern)
Whenever you display categories like this, think about the order of categories. For ranking, sort categories by value. In other cases order categories by their importance or perhaps a natural order like geography. Do not use alphabetical order - it works for phone books only ...
Time-series (trend) analysis
Time series (trend) analysis is a similar task to structure analysis but is performed when the data categories represent time (years, quarters, months, weeks, days, timestamps).
Depending on the situation, the reader will perform the following types of comparisons within the displayed time series:
- Looking up a specific value (e.g. the sales value of the last month)
- Comparing two values between each other
- Observing trends in the data (subsets of values with a specific pattern, e.g. linear growth or seasonality)
Here users try to assess the relation of a part to the whole value (e.g. sales of one product to total sales of the company). The analysis exposes the "significance" of each part relative to the total value. Use stacked bar charts for part-whole comparison (and OK, pie charts as well, but only in very limited cases!):
Avoid using pie charts altogether. They only work for a very small number of categories (1-2). In fact, they actually work best when you only display 1 category compared to the whole (as in the example above). Guess what, it's called the "pac-man" chart 😉
Variance analysis is the most fundamental task in business reporting. Understanding deviations from targets or budgets and growth rates from previous periods is a core requirement in almost any organisation. Use "plus-minus" charts to show variance over time or structures:
Use "integrated variance" charts to display actual values as well as deviations. Since both values (actual and deviation) are shown, the user can evaluate both the importance of each deviation (its magnitude) as well as its relative share of the actual value at the same time:
Wonder how to create charts like these in Excel?
Contribution Analysis is a special type of part-to-whole analysis to assess the contribution of all parts to the whole value. There are two main versions of Contribution Analysis: (1) Contribution of variances and (2) Contribution of categories.
[Tweet "If two variables are in correlation it doesn't mean there's a cause-effect relationship between them."]
Use waterfall charts for contribution analysis - they are simply perfect for explaining why a total value has changed. Here are 4 typical examples:
A waterfall chart is also called the "bridge" chart for obvious reasons.
Correlation analysis is used to assess whether two variables are correlated. It is also suitable for spotting outliers. Use scatter plot for correlation analysis:
Warning: If two variables are in correlation, this does not necessarily mean that there is a cause & effect relationship between them!
To analyse a portfolio of data categories (e.g. products, customers, suppliers, plants, etc.) by 3 variables, use the bubble chart:
#3 Determine the optimal chart shape for your data
Once you have chosen the correct chart orientation and the intended task of your visualization, you are ready for your last major decision: Which chart shape should I use?
Instead of always just using the identically looking "default" column chart regardless of what it actually represents, you should use different chart shapes for different types of values (KPIs). This is called shape coding:
[Tweet "Instead of always using the default column chart, use different chart shapes for different KPIs."]
For example, the columns in the Price chart are much thinner than the ones in the Revenue chart. This is quite intuitive - revenue values are usually way bigger than prices since the price is actually a ratio of revenue, that is, it is divided by sales quantity (a value/volume division). Similarly, you can use "pin" charts (yes, the "lollipops" 🙂 ) for percentages since they are value/value or volume/volume divisions and are thus without any unit (just indexes, %). So it seems intuitive to use chart shapes without width for indexes in business reporting. Once readers understand this, they will instantly know what they are looking at, even without reading the labels. Especially in a repeating process such as monthly reporting, this can come in very handy.
Similarly, you can use patterns to code data scenarios like Actual, Budget, Forecast and Previous Year. For example, here is how budget values can look like:
Check out IBCS (International Business Communication Standards) for more great ideas like this.
Achieving consistency in business reporting is critical!
It is relatively easy to select the perfect chart shape when you only have one chart. That's not rocket science and many researchers and authors have written guidelines on how to do this. But business reporting is so much more than just designing one single chart. In fact, most times you will have to use several charts at once to get the full message across to your readers.
Often you'll need to design a report with many pages, a BI dashboard with several screens, etc. This is when consistency in your design becomes of utmost importance.
Just as traffic signs consist of consistent colors and shapes that always mean the same thing, elements in business reports should be consistently designed to depict or "code" the same meaning with always the same shapes, colors, patterns, etc.
Emphasize single values or trends?
The shape of a chart itself determines what we will see and how we will interpret the numbers. Several "gestalt" laws govern our visual perception. That's a fact. Different chart shapes will convey different messages to your audience.
For instance, some charts are more suitable for emphasizing single values, while others are better at displaying trends. Let's illustrate this with the following examples:
While we can see the overall trend, the vertical tendency and clear indication of bar heights support us in comparing single values (compare heights between two single values, such as Jul to Aug or Nov to Dec).
Use this chart shape when the number of periods is small and only when the chart is big enough.
The flow of the line emphasizes the trend, while the dots mark the height of a single value.
There is much more emphasis on the continuation or the flow of the values (trend analysis), but still some support for single value comparisons.
There is no explicit graphical support for single value comparisons, making it very hard to estimate the differences between particular months.
What is left is the continuity of the line, clearly exposing the trend in the data.
- Use chart shapes on the left when single values are important (e.g. annual or monthly figures) and when you expect that users will compare values between each other (e.g. current month to previous month). Also note that this is only meaningful when charts are big enough that you can comfortably display the columns and labels.
- Use chart shapes on the right when you want the users to focus on overall trends. Also, choose line charts when your space is limited (e.g. small chart sizes, small category widths, etc.).
Check out Zebra BI to create the charts from this article in MS Excel with just one click.
Just remember, before you click to insert your perfect chart, think about the 3 fundamental rules!
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PDF format, 18 pages, Jan 2015