Data visualization is the art of presenting the data in a visual form using charts, graphs, maps to tell the story in a meaningful way. It’s a crucial step in the data analytics process, to begin with.
Let’s explore what data visualization is and how it can effectively present, communicate, and share data-driven insights. Apart from that let’s explore some of the most common types of data visualization, as well as the tools you can use to create them.
1. What is data visualization? A definition
It is a graphical or visual representation of data that helps to highlight the most useful insights from a dataset, making it easier to spot trends, patterns, outliers, and correlations. Data helps you to make an informed decision to drive actions and decisions- and data visualization bridges the gap. There are two broad categories of data visualization: exploration and explanation. Let’s understand both:
What are the two main types of data visualization?
Exploration vs. explanation
Exploratory data visualization helps you figure out what’s in your data by investigating the data set and identify some of its main features for a thorough analysis. This lays a foundation to spot any noteworthy trends and anomalies.
Once the analysis is done and it is figured out what the data is telling you, these insights are shared with the business stakeholders who can take action based on data. Explanatory data visualizations help you tell this story, and it’s up to you to determine which visualizations will help you to do so most effectively.
2. When should you visualize your data?
You must visualize your data any time you want to summarize and draw a conclusion based on the key findings to share it with the stakeholders and others. Data visualization usually comprises the final step in the data analysis process. It can be set out as follows:
- 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.
- Analyse the data: Determine the type of data analysis you need to carry out 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).
Five common types of data visualization:
It visualizes the relationship between two variables shown on the x-axis and the y-axis with each data point depicted as a single dot or item on the graph. Scatterplots simply describe the correlation between two variables; they don’t infer any kind of cause-and-effect relationship.
2. Bar charts
Bar charts are used to plot non-numeric data against discrete values. These non-numeric data are often used to describe certain traits or characteristics, e.g. Age group, Demographic, Target audience, etc.
3. Pie charts
Just like bar charts, pie charts are used to visualize non-numeric data. A pie chart is a circle divided into different “slices,” with each slice representing the percentage it contributes to the whole. Thus, the size of each pie slice is proportional to how much it contributes to the whole “pie.”
4. Network graphs
Network graphs show how different elements or entities within a network relate to one another, with each element represented by an individual node. These nodes are connected to other, related nodes via lines.
5. Geographical maps
Maps are an extremely versatile form of data visualization and a perfect way of communicating all kinds of location-related data. It is used to visualize the distribution of data concerning a physical and geographical area.
Overall, data visualization is an excellent skill to have, to share valuable insights with your colleagues or the stakeholders to understand your business better.