Data Storytelling: Key Factors for Chart Selection

Here are four key factors to consider when selecting a chart: (1) encoding, (2) the question you want to answer, (3) the data type(s), and (4) dimensions and metrics. Keep in mind that the main goal is to communicate information effectively through data visualization, so understanding these factors will help you design visuals that truly serve this purpose.

  1. Encoding: Encoding refers to how we represent or map the data to visual elements. These can be, for example, points, lines, areas, or colors. Various encoding choices may emphasize different aspects of the data, so it’s essential to think about the kind of message you want to convey. For instance, when comparing the population of cities, using a bar chart with length encoding can make it easier for the audience to compare the sizes rather than using color encoding on a map.
  1. The question you want to answer: Knowing the question or problem you want to address with your visualization will help guide your choice of chart type. This is essential because different charts excel at answering different types of questions. For example, if you want to compare the sales of different products over time, a line chart may be a suitable choice, as it effectively shows trends and patterns. On the other hand, if you’re looking at the distribution of customer ages, a histogram might be more appropriate.
  1. Data type(s): Knowing the data types in your dataset (e.g., numerical, categorical, ordinal, or temporal) is essential, as certain chart types work better with specific data types. For example, bar charts are excellent for comparing categorical data (e.g., comparing annual sales by product type), while scatter plots work well with continuous numerical data (e.g., exploring the relationship between customer age and spending).
  1. Dimensions and metrics: Dimensions represent categories or data points, while metrics paint a picture through numerical values. When selecting a chart, consider how many dimensions and metrics you have and how they connect. For instance, a scatter plot could effectively illustrate this multi-dimensional data if you want to show the relationship between two metrics (e.g., revenue and units sold) split by one dimension (e.g., region).


To wrap up, when selecting a chart, consider the encoding, the question you’re trying to answer, the data type(s), and the dimensions and metrics involved. However, the most important factor is always the context and purpose of your visualization.

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