Visualized Data Helps Drive Decisions: A Primer on Chart Types

This post is an introduction to a series on visualizing data.

Information drives decision-making, especially in the business world. When multiple parties are engaged in collecting intelligence, synthesizing data and determining actionable items based on results, effective ways of communicating information are critical. First, it is imperative that data are collected and synthesized using appropriate procedures. Second, it is crucial that results are summarized and presented in an accessible manner. Visualized data is one of the most effective ways to communicate insights from information and spur decision-making.

The basics of graphing data as well as core design principles can help translate quantitative and qualitative data sets into vessels of brief take-away messages. The most common types of charts are bar and column graphs, as well as pie and line charts.

Bar charts allow viewers to visually compare values across multiple categories. Because these charts present data in horizontal bars, they are especially handy when the number of categories is large or the category labels are long.

  • Good use case: Revenue generated by each member of the sales team (assuming the sales team is reasonably small).
  • Bad use case: Regional temperature averages for every month of the year. A line graph would be more appropriate here. 

Column charts, like bar graphs, allow viewers to compare values across categories. Here, however, the bars are positioned vertically rather than horizontally. Column charts are particularly useful when negative and positive values need to be represented, a handful of data points are shown, and labels of categories are short.

  • Good use case: Company revenue across the four quarters of a year.
  • Bad use case: Population across every state in the US. Here, a bar graph or even a table would be more appropriate.

Pie charts should be used to represent proportional relation between several values in a single category. Here, quantitative information is converted to percent values that add up to no more and no less than 100%.

  • Good use case: Mobile device operating system breakdown in a city (e.g. 58% iOS, 36% Android, 4% Windows, 2% Other)
  • Bad use case: Average duration of calls to the Help Desk at a business. Here, a bar or column chart would be most appropriate. Here is another example of a poor pie chart use.

Line charts are best for showing changes in data over time. A line graph can contain several sets of data, allowing the reader to compare them across time, such as company profits and cost of operations. However, multiple series of data should be presented with caution; otherwise, a reader will infer a presence or absence of a relation between visualized data that does not actually exist (see excellent examples of this problem here). Research methods should inform if and when plotting multiple series of data in a line graph is appropriate.

  • Good use case: Coffee consumption rates per department at a company during 2014.
  • Bad use case: Scores on tests measuring different variables (e.g. aesthetic appeal, ease-of-use, and level of an innovation of a website) at a single point in time. Here, a column chart would be appropriate.

Although these are the four most commonly encountered charts, their variants, such as stacked column graphs, bubble charts and treemaps also exist. These alternative chart types have their own specific use cases and should be selected only after carefully considering what type of visualization is most appropriate for the research method used to gather the data, and what insights the chart must communicate. If the amount of information that must be displayed exceeds the capabilities of these standard graphs, often the best practice is to break the data down into simpler, more easily absorbed sections. In most instances, the simplicity of design and clarity of information conveyed by a carefully chosen standard graph cannot be surpassed by their more complex variants.

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