Aside from exploratory data visualization which takes place in the early stages, data visualization usually comprises the final step in the data analysis process. To recap, the data analysis process can be set out as follows:
The five steps in the data analysis process: Defining the question, gathering your data, cleaning the data, carrying out analysis, and visualizing and sharing your findings
Define the question: What problem are you trying to solve?
Collect the data: Determine what kind of data you need and where you’ll find it.
Clean the data: Remove errors, duplicates, outliers, and unwanted data points—anything that might skew how your data is interpreted. You can learn more about data cleaning (and how to do it) in this guide.
Analyze the data: Determine the type of data analysis you need to carry out in order to find the insights you’re looking for.
Visualize the data and share your findings: Translate your key insights into visual format (e.g. graphs, charts, or heatmaps) and present them to the relevant audience(s).
Essentially, you visualize your data any time you want to summarize and highlight key findings and share them with others. With that in mind, let’s consider what kinds of insights you can convey with data visualizations.
What is data visualization used for?
Within the broader goal of conveying key insights, different visualizations can be used to tell different stories. Data visualizations can be used to:
Convey changes over time: For example, a line graph could be used to present how the value of Bitcoin changed over a certain time period.
Determine the frequency of events: You could use a histogram to visualize the frequency distribution of a single event over a certain time period (e.g. number of internet users per year from 2007 to 2021).
Highlight interesting relationships or correlations between variables: If you wanted to highlight the relationship between two variables (e.g. marketing spend and revenue, or hours of weekly exercise vs. cardiovascular fitness), you could use a scatter plot to see, at a glance, if one increases as the other decreases (or vice versa).
Examine a network: If you want to understand what’s going on within a certain network (for example, your entire customer base), network visualizations can help you to identify (and depict) meaningful connections and clusters within your network of interest.
Analyze value and risk: If you want to weigh up value versus risk in order to figure out which opportunities or strategies are worth pursuing, data visualizations—such as a color-coded system—could help you to categorize and identify, at a glance, which items are feasible.