How do you explore and generate new questions with data visualization?

1 Why ask questions with data visualization?

Asking questions is essential for any data-driven inquiry, as it helps define goals, scope, and methods, as well as uncover patterns, anomalies, and relationships in your data. Data visualization can provide multiple perspectives and dimensions of your data, reveal trends, outliers, and clusters that might be hidden in tables or numbers, enable interactive exploration and manipulation of your data, and invite curiosity and creativity to explore what-if scenarios and hypotheses.

2 How to start with data visualization?

Before you create charts and graphs, it's important to have a clear understanding of your data and your question. Start by defining your main question or objective - what are you trying to learn, solve, or communicate with your data? Then, identify the data sources and variables. Explore your data with descriptive statistics and basic plots to understand the distributions, ranges, and summary statistics of your variables. If needed, clean and transform your data to deal with any missing values, outliers, or errors; you may also need to create new variables or aggregate your data.

3 How to choose the right data visualization?

Once you have a clear question and a clean data set, you need to choose the right data visualization to answer it. There is no one-size-fits-all solution, but there are some general guidelines that can help. For instance, you should match your visualization to your data type and question type; for example, bar charts for categorical data, line charts for time series data, and scatter plots for correlation analysis. Additionally, you should use appropriate scales, axes, and labels to ensure accuracy, readability, and informativeness. Furthermore, color, shape, and size should be used wisely to enhance or highlight your data rather than distract or confuse; for instance, color can be used to show categories, groups, or values while shape and size can be used to show additional dimensions or comparisons.

4 How to explore your data visualization?

Once you have created your data visualization, you can use it to explore your data further and generate new questions. You can compare and contrast different visualizations to see how the data looks different with different types of charts, scales, or colors. Zooming in and out of your data can help you identify outliers, exceptions, or anomalies. Additionally, filtering and slicing the data can help you understand the similarities or differences between different groups or categories. Interacting with your data by manipulating, adjusting, or modifying it can also help you understand the effects or consequences of your actions.

5 How to communicate your data visualization?

Finally, you can use your data visualization to communicate your data and your questions to others. To do that, tell a story with your data and use a clear and logical structure, a compelling narrative, and a clear takeaway. Additionally, add annotations and captions to explain, clarify, or emphasize your data visualization. Also, consider the right format and medium for your audience, purpose, and channel; for example, you might need to use static or dynamic, simple or complex, or online or offline visualizations.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?